This paper introduces Model Q-II, an enhanced underactuated robotic hand designed to improve dexterous manipulation through expanded grasping modes and manipulation primitives. The Model Q-II incorporates tripod and enhanced power grasping modes, achieving increased versatility without adding additional actuators. The design employs passive mechanisms, such as lateral contact walls and a finger-locking system, to facilitate seamless transitions between modes, enabling precise pinch-to-tripod and pinch-to-power gating. These enhancements allow the hand to perform complex in-hand manipulations, including multi-directional object positioning. Theoretical analysis, simulations, and experimental evaluations validate the hand's performance, demonstrating improved grasping force, range, and manipulation capabilities. The results highlight Model Q-II's ability to handle various tasks, offering a robust, cost-effective solution for applications requiring both precise and powerful grasping.

Camera-to-robot (also known as eye-to-hand) calibration is a critical component of vision-based robot manipulation. Traditional marker-based methods often require human intervention for system setup. Furthermore, existing autonomous markerless calibration methods typically rely on pre-trained robot tracking models that impede their application on edge devices and require fine-tuning for novel robot embodiments. To address these limitations, this paper proposes a model-based markerless camera-to-robot calibration framework, ARC-Calib, that is fully autonomous and generalizable across diverse robots and scenarios without requiring extensive data collection or learning. First, exploratory robot motions are introduced to generate easily trackable trajectory-based visual patterns in the camera’s image frames. Then, a geometric optimization framework is proposed to exploit the coplanarity and collinearity constraints from the observed motions to iteratively refine the estimated calibration result. Our approach eliminates the need for extra effort in either environmental marker setup or data collection and model training, rendering it highly adaptable across a wide range of real-world autonomous systems. Extensive experiments are conducted in both simulation and the real world to validate its robustness and generalizability.

Traditional robot control relies on analytical methods that require precise system models, which are hard to apply in real-world settings and limit generalization to arbitrary tasks. However, systems like serial manipulators and passively adaptive hands feature inherently stable regions without control discontinuities like loss of contact or singularities. In these regions, approximate controllers focusing on the correct direction of motion enable successful coarse manipulation. When coupled with a rough estimation of the motion magnitude, precision manipulation is achieved. Leveraging this insight, we introduce a novel inverse Jacobian estimation method that independently estimates the primary motion direction and magnitude of the manipulator’s actuators. Our method efficiently estimates the direct mapping from task to actuator space with no need for a priori system knowledge enabling the same framework to control both hands and arms without compromising task performance. We present a novel control method with no a priori knowledge for precision manipulation. Experiments on the Yale Model O hand, Yale Stewart Hand, and a UR5e arm demonstrate that the inverse Jacobians estimated via our approach enable real-time control with submillimeter precision in manipulation tasks. These results highlight that online self-ID data alone is sufficient for precise real-world manipulation.

The digitization of natural history collections over the past three decades has unlocked a treasure trove of specimen imagery and metadata. There is great interest in making this data more useful by further labeling it with additional trait data, and modern "deep learning" machine learning techniques utilizing convolutional neural nets (CNNs) and similar networks show particular promise to reduce the amount of required manual labeling by human experts, making the process much faster and less expensive. However, in most cases, the accuracy of these approaches is too low for reliable utilization of the automatic labeling, typically in the range of 80-85% accuracy. In this paper, we present and validate an approach that can greatly improve this accuracy, essentially by examining the "confidence" that the network has in the generated label as well as utilizing a user-defined threshold to reject labels that fall below a chosen level. We demonstrate that a naive model that produced 86% initial accuracy can achieve improved performance - over 95% accuracy (rejecting about 40% of the labels) or over 99% accuracy (rejecting about 65%) by selecting higher confidence thresholds. This gives flexibility to adapt existing models to the statistical requirements of various types of research and has the potential to move these automatic labeling approaches from being unusably inaccurate to being an invaluable new tool. After validating the approach in a number of ways, we annotate the reproductive state of a large dataset of over 600,000 herbarium specimens. The analysis of the results points at under-investigated correlations as well as general alignment with known trends. By sharing this new dataset alongside this work, we want to allow biologists to gather insights for their own research questions, at their chosen point of accuracy/coverage trade-off.

Precision gas analyzers are widely used in ecological research for manual measurement of soil carbon flux, a key metric used in the study of climate change. We present a generational update to the first low-cost, autonomous, closed-chamber style soil CO2 flux sensors (Fluxbots). Fluxbot 2.0 is the first such low-cost autonomous flux chamber capable of real-time wireless data transmission, which enables ecologists conducting in situ soil carbon flux surveys to set up their own wireless sensor arrays, reporting carbon flux data in real time at a very high level of temporal resolution. The system’s low cost (less than 500 USD per unit) and long-range cellular data transmission capabilities also allow for greatly improved spatial resolution. Additionally, the updated system consumes significantly less power, resulting in the ability to be deployed for longer than 10 × the battery lifetime of the original version on a single charge.

The ability to plan and control robotic in-hand manipulation is challenged by several issues, including the required amount of prior knowledge of the system and the sophisticated physics that varies across different robot hands or even grasp instances. One of the most direct models of in-hand manipulation is the inverse Jacobian, which can directly map from the desired in-hand object motions to the required hand actuator controls. However, acquiring such inverse Jacobians without complex hand-object system models is typically infeasible. We present a method for controlling in-hand manipulation using inverse Jacobians that are self-identified by a particle filter-based estimation scheme that leverages the ability of underactuated hands to maintain a passively stable grasp during self-identification movements. This method requires no a priori knowledge of the specific hand-object system and learns the system’s inverse Jacobian through small exploratory motions. Our system approximates the underlying inverse Jacobian closely, which can be used to perform manipulation tasks across a range of objects successfully. With extensive experiments on a Yale Model O hand, we show that the proposed system can provide accurate in-hand manipulation of sub-millimeter precision and that the inverse Jacobian-based controller can support real-time manipulation control of up to 900Hz.

This systems paper presents the implementation and design of RB5, a wheeled robot for autonomous long-term exploration with fewer and cheaper sensors. Requiring just an RGB-D camera and low-power computing hardware, the system consists of an experimental platform with rocker-bogie suspension. It operates in unknown and GPS-denied environments and on indoor and outdoor terrains. The exploration consists of a methodology that extends frontier- and sampling-based exploration with a path-following vector field and a state-of-the-art SLAM algorithm. The methodology allows the robot to explore its surroundings at lower update frequencies, enabling the use of lower-performing and lower-cost hardware while still retaining good autonomous performance. The approach further consists of a methodology to interact with a remotely located human operator based on an inexpensive long-range and low-power communication technology from the internet-of-things domain (i.e., LoRa) and a customized communication protocol. The results and the feasibility analysis show the possible applications and limitations of the approach.Code—The open-source software stack is made available on the project repository webpage†.

Continuous exploration without interruption is important in scenarios such as search and rescue and precision agriculture, where consistent presence is needed to detect events over large areas. Ergodic search already derives continuous trajectories in these scenarios so that a robot spends more time in areas with high information density. However, existing literature on ergodic search does not consider the robot's energy constraints, limiting how long a robot can explore. In fact, if the robots are battery-powered, it is physically not possible to continuously explore on a single battery charge. Our paper tackles this challenge, integrating ergodic search methods with energy-aware coverage. We trade off battery usage and coverage quality, maintaining uninterrupted exploration by at least one agent. Our approach derives an abstract battery model for future state-of-charge estimation and extends canonical ergodic search to ergodic search under battery constraints. Empirical data from simulations and real-world experiments demonstrate the effectiveness of our energy-aware ergodic search, which ensures continuous exploration and guarantees spatial coverage.

Calibrating robots into their workspaces is crucial for manipulation tasks. Existing calibration techniques often rely on sensors external to the robot (cameras, laser scanners, etc.) or specialized tools. This reliance complicates the calibration process and increases the costs and time requirements. Furthermore, the associated setup and measurement procedures require significant human intervention, which makes them more challenging to operate. Using the built-in force-torque sensors, which are nowadays a default component in collaborative robots, this work proposes a self-calibration framework where robot-environmental spatial relations are automatically estimated through compliant exploratory actions by the robot itself. The self-calibration approach converges, verifies its own accuracy, and terminates upon completion, autonomously purely through interactive exploration of the environment’s geometries. Extensive experiments validate the effectiveness of our self-calibration approach in accurately establishing the robot-environment spatial relationships without the need for additional sensing equipment or any human intervention.

Contact can be conceptualized as a set of constraints imposed on two bodies that are interacting with one another in some way. The nature of a contact, whether a point, line, or surface, dictates how these bodies are able to move with respect to one another given a force, and a set of contacts can provide either partial or full constraint on a body's motion. Decades of work have explored how to explicitly estimate the location of a contact and its dynamics, e.g., frictional properties, but investigated methods have been computationally expensive and there often exists significant uncertainty in the final calculation. This has affected further advancements in contact-rich tasks that are seemingly simple to humans, such as generalized peg-in-hole insertions. In this work, instead of explicitly estimating the individual contact dynamics between an object and its hole, we approach this problem by investigating compliance-enabled contact formations. More formally, contact formations are defined according to the constraints imposed on an object's available degrees-of-freedom. Rather than estimating individual contact positions, we abstract out this calculation to an implicit representation, allowing the robot to either acquire, maintain, or release constraints on the object during the insertion process, by monitoring forces enacted on the end effector through time. Using a compliant robot, our method is desirable in that we are able to complete industry-relevant insertion tasks of tolerances <0.25mm without prior knowledge of the exact hole location or its orientation. We showcase our method on more generalized insertion tasks, such as commercially available non-cylindrical objects and open world plug tasks.

Robot manipulation today generally focuses on motions exclusively with a robot arm or a dexterous hand, but usually not a combination of both. However, complex manipulation tasks can require coordinating arm and hand motions that leverage capabilities of both, much like the coordinated arm and hand motions carried out by humans to perform everyday tasks. In this work, we evaluate unified manipulation with robot arms and dexterous hands, using a motion optimization framework that synthesizes a series of configuration states over the entire manipulation system. We characterize the possible benefits of unifying arm and dexterous hand capabilities within a single model via metrics such as pose accuracy, manipulability, joint-space smoothness, distance to joint-limits, distance to collisions, and more. Several arm-hand combinations are quantitatively compared in simulation on a variety of experiment tasks and performance measures. Our results suggest that combining motions from robot arms and dexterous hands indeed has compelling benefits, highlighting the exciting potential of continued progress in unified arm-hand motion synthesis for robotics applications.

Building hand-object models for dexterous in-hand manipulation remains a crucial and open problem. Major challenges include the difficulty of obtaining the geometric and dynamical models of the hand, object, and time-varying contacts, as well as the inevitable physical and perception uncertainties. Instead of building accurate models to map between the actuation inputs and the object motions, this work proposes to enable the hand-object systems to continuously approximate their local models via a self-identification process where an underlying manipulation model is estimated through a small number of exploratory actions and non-parametric learning. With a very small number of data points, as opposed to most data-driven methods, our system self-identifies the underlying manipulation models online through exploratory actions and non-parametric learning. By integrating the self-identified hand-object model into a model predictive control framework, the proposed system closes the control loop to provide high accuracy in-hand manipulation. Furthermore, the proposed self-identification is able to adaptively trigger online updates through additional exploratory actions, as soon as the self-identified local models render large discrepancies against the observed manipulation outcomes. We implemented the proposed approach on a sensorless underactuated Yale Model O hand with a single external camera to observe the object's motion. With extensive experiments, we show that the proposed self-identification approach can enable accurate and robust dexterous manipulation without requiring an accurate system model nor a large amount of data for offline training.

Substantial advancements to model-based reinforcement learning algorithms have been impeded by the model-bias induced by the collected data, which generally hurts performance. Meanwhile, their inherent sample efficiency warrants utility for most robot applications, limiting potential damage to the robot and its environment during training. Inspired by information theoretic model predictive control and advances in deep reinforcement learning, we introduce Model Predictive Actor-Critic (MoPAC)†, a hybrid model-based/model-free method that combines model predictive rollouts with policy optimization as to mitigate model bias. MoPAC leverages optimal trajectories to guide policy learning, but explores via its model-free method, allowing the algorithm to learn more expressive dynamics models. This combination guarantees optimal skill learning up to an approximation error and reduces necessary physical interaction with the environment, making it suitable for real-robot training. We provide extensive results showcasing how our proposed method generally outperforms current state-of-the-art and conclude by evaluating MoPAC for learning on a physical robotic hand performing valve rotation and finger gaiting–a task that requires grasping, manipulation, and then regrasping of an object.

Rotating a grasped object about all three spatial axes is challenging, because kinematically redundant robot hands require complex control schemes for within-hand rotations, and simple parallel grippers require inefficient whole arm motions. We present a novel 3-finger robot hand design inspired by a spherical parallel mechanism that achieves these rotations with just 3 actuators. The hand is designed such that at every hand-object configuration, the object pose moves along a sphere with a fixed center, which is determined by the intersection of the fingers’ revolute axes and is independent of the object shape, pose, and the initial grasp configuration. We optimize the hand based on 3-RRS spherical manipulator to maximize both its rotational workspace size and manipulation motion quality. From these parameters, we implement and experimentally evaluate the hand design through grasping tests, manipulation characterization, and real-world task scenarios, which show that the hand is able to grasp a variety of object geometries and accomplish precise single and multi-DOF rotations about a fixed point. We believe this design can remarkably improve robustness and simplify control for dexterous within-hand rotations, which finds utility in augmenting the capabilities of low-DOF robot arms without an active wrist.

Learning accurate representations of robot models remains a challenging problem, and is typically approached though large, system-specific feature sets. This method inherently introduces practical shortcomings, as interpretability and transferability of the learned model typically decreases as more features are introduced into the learning framework in order to handle increasing task complexity. In this article, we examine the problem of developing transferable learned models for dexterous manipulation that are able to accurately predict the behavior of physically distinct systems without retraining. We introduce the notion of learning from visually-extracted grasp mechanics-based features, which are formulated by combining geometrically-inspired, analytical representations of the gripper into the feature set to more holistically represent the state of varied systems performing manipulation. We characterize the added utility of using such features through simulation and incorporate them into a classifier to predict specific phenomena, or modes of manipulation, that occur during prehensile within-hand movement. Four modes of manipulation—normal (rolling contact), drop, stuck, and sliding—are defined, collected physically, and trained via a self-supervised learning approach. The classifier is first trained on a single sensorless underactuated hand variant for all four modes. We, then, investigate the transferability of the learned classifier on five different planar gripper variants—analyzing applicability of this approach with both online and offline evaluation.

To date, many control strategies for legged robots have been proposed for stable locomotion over rough and unstructured terrains. However, these approaches require sensing information throughout locomotion, which may be noisy or unavailable at times. An alternative solution to rough terrain locomotion is a legged robot design that can passively adapt to the variations in the terrain without requiring knowledge of them. This paper presents one such solution in the design of a walking robot that employs pin array mechanisms to passively adapt to rough terrains. The pins are passively dropped over the terrain to conform to its variations and then locked to provide a statically stable stance. Locomotion is achieved with parallel four-bar linkages that swing forward the platforms in an alternating manner. Experimental evaluation of the robot demonstrates that the pin arrays enable legged locomotion over rough terrains under open-loop control.

Grasping can be conceptualized as the ability of an end-effector to temporarily attach or fixture an object to a manipulator-constraining all motion of the workpiece with respect to the end-effector's base frame. This seemingly simplistic action often requires excessive sensing, computation, or control to achieve with multi-fingered hands, which can be mitigated with underactuated mechanisms. In this work, we present the analysis of radial graspers for automated part fixturing and grasping in the plane with a design implementation of a single-actuator, 8-finger gripper. By leveraging a passively adaptable mechanism that is under-constrained pre-contact, the gripper conforms to arbitrary object geometries and locks post-contact as to provide form closure around the object. We also justify that 8 radially symmetric fingers with passive locking are sufficient to create robust form closure grasps on arbitrary planar objects. The underlying mechanism of the gripper is described in detail, with analysis of its highly underactuated nature, and the resulting form closure ability. We show with a wide variety of objects that the gripper is able to acquire robust grasps on all of them, and maintain maximal quality form closure on most objects, with each finger exerting equal grasp force within ±2.48 N.

In this paper we address the problem of creating planar caging grasps on objects using simple, underactuated grippers with no sensing or control. Specifically, we examine how changes in mechanical compliance, passive adaptability due to underactuation, and finger phalanx length affect the ability to create caging grasps passively, by altering the free-swing motion of the fingers. We present a simple model for simulating the underactuated hand, develop a metric for quantifying a hand design's caging ability, and perform a design parameter space search to reveal the important design factors influencing passive caging behavior. The results show that both palm width and the interplay between joint spring stiffness and pulley radius ratios play the largest roles in determining caging behavior. The effect of varying design parameters on the caging grasp performance of the hand is discussed, the best resulting design is shown, and a list of principles to guide the design of simple underactuated hands for caging grasps is presented.

In this work, we present a within-hand manipulation approach that leverages a simple energy model based on caging grasps made by underactuated hands. Instead of explicitly modeling the contacts and dynamics in manipulation, we can calculate a map to describe the energy states of different hand-object configurations under an actuation input. Since the system intrinsically steers towards low energy states, the object's movement is uniquely described by the gradient of the energy map if the corresponding actuation is applied. Such maps are pre-calculated for a range of actuation inputs to represent the system's energy profile. We discretize the workspace into a grid and construct an energy gradient-based graph by locally exploring the gradients of the stored energy profile. Given a goal configuration of a simple cylindrical object, a sequence of actuation inputs can be calculated to manipulate it towards the goal by exploiting the connectivity in the graph. The proposed approach is experimentally implemented on a Yale T42 hand. Our evaluation results show that parts of the graph are well connected, explaining our ability to successfully plan and execute trajectories within the gripper's workspace.

In this paper we present a novel method of categorizing naturalistic human arm motions during activities of daily living using clustering techniques. While many current approaches attempt to define all arm motions using heuristic interpretation, or a combination of several abstract motion primitives, our unsupervised approach generates a hierarchical description of natural human motion with well recognized groups. Reliable recommendation of a subset of motions for task achievement is beneficial to various fields, such as robotic and semi-autonomous prosthetic device applications. The proposed method makes use of well-known techniques such as dynamic time warping (DTW) to obtain a divergence measure between motion segments, DTW barycenter averaging (DBA) to get a motion average, and Ward's distance criterion to build the hierarchical tree. The clusters that emerge summarize the variety of recorded motions into the following general tasks: reach-to-front, transfer-box, drinking from vessel, on-table motion, turning a key or door knob, and reach-to-back pocket. The clustering methodology is justified by comparing against an alternative measure of divergence using Bezier coefficients and K-medoids clustering.

In this paper, we present a constrained optimization framework for evaluating the post-contact stability of underactuated precision grasping configurations with a single degree of actuation. Relationships between key anthropomorphic design parameters including link length ratios, transmission ratios, joint stiffness ratios and palm width are developed with applications in upper limb prosthetic design. In addition to grasp stability, we examine post-contact system work, to reduce reconfiguration, and consider the range of objects that can be stably grasped. External wrenches were simulated on a subset of the heuristically evaluated optimal solutions and an optimal configuration was experimentally tested to determine favorable wrench resistible gripper orientations for grasp planning applications.

In this work, we investigate a mechanics-inspired framework for describing fingertip-based planar within-hand manipulation with an underactuated robotic gripper. In particular, this framework leverages fundamental mechanics properties of the hand-object system, including basic terms such as local contact curvature as well as more complex features including the grasp matrix and manipulability metrics. These are extracted using a simple visual approach and then in real-time used for predicting planar manipulation modes: namely rolling, dropped, stuck, and sliding. Given a desired cartesian motion for the object, a supervised learning model predicts these four manipulation modes before they occur, allowing us to either avoid or trigger these different behaviors. Since we utilize strictly fundamental properties of the grasp matrix, finger Jacobians, and contact curvatures, we are able to demonstrate prediction transferability between different grippers using our original classifier. In particular, a Random Forests classifier trained on one gripper successfully predicts manipulation modes for grippers with different fingers with 84% accuracy, compared to just 56% from an approach in previous work. Overall, we find that the features designed in our approach better describes fingertip manipulation when precise gripper models are not available.

We address the problem of developing precision, quasi-static control strategies for fingertip manipulation in robot hands. In general, analytically specifying useful object transition maps, or hand-object Jacobians, for scenarios in which there is uncertainty in some key aspect of the hand-object system is difficult or impossible. This could be in scenarios with standard fully-actuated hands where, for instance, there is not an accurate model of the contact conditions, or in scenarios with fewer control inputs than mechanical degrees of freedom (such as underactuated hands or those that are controlled by synergies or impedance controlled frameworks), since the output space is of higher dimension than the input space. In this work, we develop a method for extracting object transition maps by tracking the state of the grasp frame. We begin by modeling a compliant, underactuated hand and its mechanical properties through an energy-based approach. From this energy model, we provide controlled actuation inputs to change the state of the grasp frame. We observe the response from these actions and develop a regression map of the action-reaction pairs, where the map is subject to our intent for grasp frame movement and the regional relationship between the contacts. Once the regression model is developed, we perform within-hand planning of the grasp frame with newly introduced objects. This approach is agnostic to the global geometry of the object and is able to adapt when undesirable contact conditions, such as sliding, occur. The learning-based methodology estimates the non-linearities representative in the properties of the system. We test our framework physically on an adapted Yale Openhand Model O. By transferring the learned model from simulation to the physical hand without adaptation, we show that this energy modeling approach is robust to inaccuracies in parameter estimation. We demonstrate its efficacy in a handwriting task.

This paper presents a high-performance vision-based precision manipulation technique that does not rely on an object, contact, or gripper model, which are challenging and often times impractical to acquire. Instead, we utilize a simple process model that roughly maps object velocities to actuator velocities, and we maintain system efficiency and robustness via advanced vision-based control techniques with disturbance rejection mechanisms. For obtaining simple models, we derive a set of actuator coordination rules for achieving common task space motions. The performance degradation due to modeling inaccuracies is then minimized via the model predictive control framework and a correction matrix method. Our experimental results show that the proposed strategy results in high-performance precision manipulation with minimal modeling effort.

In this paper, we focus on the formulation of a hybrid methodology that combines analytical models, constrained optimization schemes, and machine learning techniques to simplify the execution of dexterous, in-hand manipulation tasks with adaptive robot hands. More precisely, the constrained optimization scheme is used to describe the kinematics of adaptive hands during the grasping and manipulation processes, unsupervised learning (clustering) is used to group together similar manipulation strategies, dimensionality reduction is used to either extract a set of representative motion primitives (for the identified groups of manipulation strategies) or to solve the manipulation problem in a low-d space and finally an automated experimental setup is used for unsupervised, automated collection of large data sets. We also assess the capabilities of the derived manipulation models and primitives for both model and everyday life objects, and we analyze the resulting manipulation ranges of motion (e.g., object perturbations achieved during the dexterous, in-hand manipulation). We show that the proposed methods facilitate the execution of fingertip-based, within-hand manipulation tasks while requiring minimal sensory information and control effort, and we demonstrate this experimentally on a range of adaptive hands. Finally, we introduce DexRep, an online repository for dexterous manipulation models that facilitate the execution of complex tasks with adaptive robot hands.

The human wrist contributes greatly to the mobility of the arm/hand system, empowering dexterity and manipulation capabilities. However, both robotic and prosthetic research communities tend to favor the study and development of end-effectors/terminal devices (hands, grippers, etc.) over wrists. Wrists can improve manipulation capabilities, as they can orient the end-effector of a system without imparting significant translational motion. In this paper, we review the current state of the art of wrist devices, ranging from passive wrist prostheses to actuated robotic wrist devices. We focus on the mechanical design and kinematic arrangements of said devices and provide specifications when available.

Human in-hand dexterity can be highly fluid and unstructured, with multiple phalanxes breaking and re-establishing contact during any given task. In contrast, prevailing research in robotic manipulation has focused on highly structured well-controlled motions, where contact points are carefully characterized. Maintaining grasp stability by satisfying traditional closure conditions during complex within-hand manipulation motions can be difficult, even with highly articulated end effectors. However, simple grippers can still achieve an effective range of in-hand manipulation tasks without strict closure conditions, as long as the object can be bounded locally relative to the hand frame. The end effector can be considered as a tool to limit the range of possible object poses. In particular, the energy of the hand-object system can be used to determine an attractor region toward which the hand drives the object. This can be combined with a sparse sampling of the configuration space to find a set of manipulation primitives that can reliably constrain the object inside the hand workspace even without feedback, a strategy proposed as whole-hand caging manipulation. In this paper, experimental results with a planar underactuated gripper are presented to validate this manipulation strategy, and it is shown that even though contacts are regularly broken and reformed, the object can be reliably manipulated within the hand workspace without ejection, and challenging movements such as sliding and gaiting can be reliably performed.

This paper discusses the kinematic design and geometric optimization of a novel hybrid three degree-of-freedom (DOF) wrist mechanism. The architecture consists of a one prismatic-revolute-universal linkage and one prismatic-spherical-spherical linkage in parallel with a revolute-universal linkage. This architecture is capable of spherical motion identical to that of a pitch-yaw-roll wrist. Moreover, this mechanism is considered to be partially decoupled, as not all actuators contribute to motion in an arbitrary direction. The forward and inverse kinematics of the parallel 2-DOF mechanism are presented. The 2-DOF mechanism is geometrically optimized over its design parameters to maximize a global transmission index, which measures the motion and torque transmissibility of particular wrist configuration over its workspace. The decoupled nature of the mechanism allows the pitch and yaw mechanism to be optimized separately, greatly reducing the parameter search space and allowing a much larger number of mechanism configurations to be simulated. We leverage this increase in simulated configurations to examine the effect of size constraints on the resulting mechanisms as well.

In this work, we investigate methods to detect four phenomena (modes) that occur during prehensile fingertip-based within-hand manipulation without the use of tactile sensors. By using actuator states and visual data, we aim to recognize different modes of operation such as interpreting if the hand is about to drop the object, if the object will begin to slide on the fingers, or if the system is at or near a singularity. For this purpose, we utilize supervised learning techniques, which allow us to detect the modes without the use of a mechanical model of the system. We analyze the individual roles of specific features available through both the actuator and visual data, and identify the ones that have the most significance for detecting the operation modes. Our results show classification performance of 96% (using either Extra Trees, Gradient Boosting, or SVM) when using combined actuator and visual features. Interestingly, we were able to achieve a 94%classification rate using only actuator information, and 93 % using only visual information. Overall, the classifiers identified actuator positions, actuator loads, and commanded velocities as the most important features for detecting a mode. These results have implications for enabling the control of within-hand manipulation movements utilizing a minimal amount of sensory information without a model of the hand/object system.

In this paper, we present a methodology based on constrained optimization methods for estimating and compensating for post-contact parasitic object motions for underactuated, compliant robot hands and for deriving stable, minimal effort grasps to try to minimize these movements. To do so, we compute the object motions for different hand designs, object shapes, and object sizes and we synthesize appropriate robot arm trajectories that eliminate them, even in hands with complex flexure-based compliant members. The effectiveness of the proposed methods is validated using a seven DOF robot arm (Barrett WAM) and a range of compliant underactuated robot hands (Yale OpenHand models T42PP, T42PF, and T42FF).

Automated grasping has a long history of research that is increasing due to interest from industry. One grand challenge for robotics is Universal Picking: the ability to robustly grasp a broad variety of objects in diverse environments for applications from warehouses to assembly lines to homes. Although many researchers now openly share code and data, it is challenging to compare and/or reproduce experimental results to identify which aspects of which approaches work best due to variations in assumptions and experimental protocols, e.g., sensors, lighting, robot arms, grippers, and objects.

It is very challenging for a robotic gripper to achieve large reorientations with grasped objects without accidental object ejection. This paper presents a simple gripper that can repeatedly achieve large reorientations over π/2 rad through the kinematics of the hand-object system alone, without the use of high fidelity contact sensors, complex control of active finger surfaces, or highly actuated fingers. This gripper is the result of two kinematic parameter search optimizations connected in cascade. Besides the large range of reorientation attained, the obtained gripper also corresponds to a novel topology since ternary joints in the palm are presented. The in-hand planar reorientation capabilities of the proposed gripper are experimentally tested with success.

Precision manipulation with underactuated hands is a challenging problem due to difficulties in obtaining precise gripper, object and contact models. Using vision feedback provides a degree of robustness to modeling inaccuracies, but conventional visual servoing schemes may suffer from performance degradation if inaccuracies are large and/or unmodeled phenomena (e.g. friction) have significant effect on the system. In this paper, we propose the use of Model Predictive Control (MPC) framework within a visual servoing scheme to achieve high performance precision manipulation even with very rough models of the manipulation process. With experiments using step and periodic reference signals (in total 204 experiments), we show that the utilization of MPC provides superior performance in terms of accuracy and efficiency comparing to the conventional visual servoing methods.

This paper studies the synthesis of between-leg coupling schemes for passively-adaptive non-redundant legged robots. Highly actuated legged robots can arbitrarily locate their feet relative to their bodies through active control, but often wind up kinematically over-constrained following ground contact, requiring complex redundant control for stable locomotion. The use of passive sprung joints can provide some minimal passive adaptability to terrain, but it is limited to relatively low terrain variability due to practical travel limits. In this paper, using a 4-RR platform as case study, we show that implementing parallel adaptive couplings between legs of a stance platform can yield substantial passive adaptability to rough terrain while still ensuring that the body is fully constrained in stance. This study uses screw theory-based mobility analysis methods to determine the number of constraints required to control the stance platform. Several coupling schemes are then considered and evaluated through a simulation of their stance capabilities over arbitrary terrain. An experimental validation of these simulation results is presented; it demonstrates the viability of the proposed scheme for passive adaptability.

Human in-hand dexterity can be highly fluid and unstructured, but in contrast, prevailing research in robotic manipulation has focused on highly structured, well-controlled motions where contact points are carefully characterized. Maintaining grasp stability through traditional closure conditions during complex within-hand manipulation motions can be difficult, even with highly-articulated end-effectors. However, simple grippers can still achieve an effective range of in-hand manipulation tasks without strict closure conditions, as long as the object can be bounded locally relative to the hand frame. The end-effector can be utilized as a tool to limit the range of possible object poses. We show that the hand-object system's configuration space can be sampled to find a set of manipulation primitives that can reliably constrain the object inside the hand workspace even without feedback, a strategy proposed as whole-hand caging manipulation. Experimental results with a planar (gravity into the page), two-finger underactuated gripper (Yale OpenHand) are presented to validate this manipulation strategy, and it is shown that even though contacts are regularly broken and reformed, the object can be repeatability manipulated within the hand workspace without ejection, enabling challenging behaviors such as sliding and gaiting.

A new class of simple, adaptive, under-actuated and compliant robot hands has recently attracted the interest of the robotics community. The under-actuated mechanisms and the structural compliance used in these hands facilitate and robustify not only grasping but also the execution of dexterous, in-hand manipulation tasks. Another significant characteristic of the particular hands is that they are able to efficiently grasp a wide range of everyday life objects even under significant object pose uncertainties. However, these hands, are difficult to model due to kinematic constraints introduced by the underactuation and the use of complex flexure joints. Moreover, adaptive hands tend to reconfigure upon contact with the object surface, imposing certain parasitic object motions. In this paper, we propose a learning scheme that uses the contact force measurements collected from tactile sensors to estimate the post-contact reconfiguration of the hand-object system and the imposed parasitic object motion. The learning scheme's estimates are compared with “ground truth” data that describe the actual motion of the object and that are collected using a vision based motion capture system. The proposed learning scheme can be used with any type of adaptive robot hand and its efficiency is experimentally validated using extensive paradigms involving different hand designs and various everyday life objects.

Adaptive robot hands have changed the way we approach and think of robot grasping and manipulation. Traditionally, pinch, fingertip grasping and dexterous, in-hand manipulation tasks were executed with fully actuated, rigid robot hands and relied on analytic methods, computation of the hand object Jacobians and extensive numerical simulations for deriving optimal and minimal effort grasps. However, even insignificant uncertainties in the modeling space could render the extraction of candidate grasps or manipulation paths infeasible. Adaptive hands use underactuated mechanisms and structural compliance, facilitating by design the successful extraction of stable grasps and the robust execution of manipulation tasks, even under significant object pose or other environmental uncertainties. In this paper, we propose a methodology for the automated extraction of dexterous, in-hand manipulation strategies / primitives for adaptive hands. To do so, we use a constrained optimization scheme that describes the kinematics of adaptive hands during the grasping and manipulation processes, an automated experimental setup for data collection, a clustering technique that groups together similar manipulation strategies, and a dimensionality reduction technique that projects the robot kinematics to lower dimensional manifolds. In these manifolds, control is simplified and hand operation becomes more intuitive. In this work, we also assess the effect of the extracted manipulation primitives on the object pose perturbations. The efficiency of the proposed methods is experimentally verified for various adaptive robot hands. The extracted primitives can simplify the operation and control of the open-source robot hand designs of the Yale Open Hand project in dexterous manipulation tasks.

In this paper, we present a novel robotic hand specifically designed for dexterous spatial manipulation. Unlike most other dexterous hand designs that attempt to mimic the kinematic structure of the human hand, the proposed mechanism instead is based on the non-anthropomorphic “hexapod” structure of the Stewart platform parallel manipulator. The hand is composed of a single “grasp” actuator connected via an underactuated differential mechanism to six prismatic actuators arranged into pairs, forming three planar, parallel mechanism fingers that replicate the kinematics of a traditional 6-degree of freedom Stewart platform when grasping an object. This configuration allows for both grasping and accurate manipulation of a range of object sizes/shapes with minimal sensing. We describe the design and fabrication of a prototype hand that incorporates these kinematics and experimentally demonstrate it performing a series of accurate translational and rotational manipulations.

In this paper we investigate a series of candidate regular planar mesh geometries in terms of their suitability for utilization as mesh primitives for constructing compliant robotic structures. In prior work, we established a framework of compliant, articulate robotic meshes, termed Modular Active Cell Robots (MACROs), created from contractile Shape Memory Alloy linear-actuators (Active Cells). In this paper, we examine how to utilize these MACROs and other mesh-like robots in large regular or semi-regular topologies. We evaluate axial strains and stiffness characteristics for a range of MACRO meshes created using different mesh-primitives, which are drawn from known regular space-filling lattice geometries. We then describe the implications of these results on the design of MACRO structures, including the tradeoffs between different primitives for various structural performance properties.

In this paper, we present the design of a shape-memory-alloy (SMA)-based compliant linear actuator [active cell (AC)] and the use of these in designing and modeling articulated meshes, which form the mechanical subsystem of a class of proposed modular active-cell robots (MACROs). The ACs are capable of undergoing ~25% strain and groups of cells are connected via passively compliant nodes to produce articulated mesh networks. The deformation of compliant meshes of ACs is modeled by scale-invariant parametric equations derived from the physics of SMA deformations and a reduced-order model of the cells. Parameters of the implemented system were used to develop a simulation platform that predicts the mechanical deformation of the networked robot given electrical inputs at arbitrary nodes of the network. We provide results of several experimental trials used to validate and establish the accuracy of this deformation model. The error in predicting deformations in small meshes is shown to be under 10% over both time-varying inputs and at steady states.

While designing robot hands based on grasping data is more common, fewer previous works have used details of human manipulation kinematics to improve robot hand design. The current work involves an underactuated, tendon driven, anthropomorphic manipulator with two flexor tendons and an abduction-adduction tendon, and describes its design based on experimental human precision manipulation data. Link lengths, joint axis alignment, and moment arms were derived from human subject data and values in the literature. The spring ratios, determining the torque relationships between joints, were then selected to maximize the achievable manipulation workspace from the human trial without requiring large forces, which are likely to lead to instability and object ejection. This is done by minimizing the stored spring energy in the robotic fingers across the range of precision manipulation workspace positions achieved by a representative human subject. After fabricating the hand, the energy characteristics of the resulting prototype are analyzed, and the robotic workspace is compared against the original human one. Despite only having three actuators, the hand is able to manipulate the test object within a 2.7 cm3workspace volume, compared to an average human workspace of 5.4 cm3for the same object. Future work could include adding antagonist actuators to achieve a larger motion range along the palmar-dorsal axis, which is currently the most limited axis of motion in comparison to the original human workspace.

This paper presents a minimalistic handheld haptic interface designed to provide pedestrian navigation assistance via the intuitive and unobtrusive stimulus of shape-changing. The new device, named the Haptic Taco, explores a novel region of robotic interfaces which we believe to have benefits over other communication methods. In previous work, we demonstrated the use of a 2DOF shape changing interface for navigation without the use of sight. In this paper we seek to explore the potential of minimal 1DOF interfaces, whose simplicity may increase intuitiveness and performance despite conveying less information. The Haptic Taco utilizes the same `variable volume' concept as a previous device, the Haptic Lotus (2010), but with reduced body compliance and higher force exertion capability. Both devices modulate their perceived volume in relation to proximity to a navigational target (a destination or waypoint). As users walk within an environment, they also attempt to minimize the device volume, finding targets via an embodied `steepest descent' method. Experimental comparison of the Lotus and Taco in a target-finding study revealed that the Taco interface increased motion path efficiency by 24% over the Lotus, to 47% average efficiency. This result is highly comparable to the mean motion efficiency of 43.6-48% observed in prior experiments with the 2DOF shape-changing interface, the Animotus. The findings indicate the potential for minimalistic interfaces in this emerging field.

In this paper, a method is proposed for vision-based within-hand precision manipulation with underactuated grippers. The method combines the advantages of adaptive underactuation with the robustness of visual servoing algorithms by employing simple action sets in actuator space, called precision manipulation primitives (PMPs). It is shown that, with this approach, reliable precision manipulation is possible even without joint and force sensors by using only minimal gripper kinematics information. An adaptation method is also utilized in the vision loop to enhance the system's transient performance. The proposed methods are analyzed with experiments using various target objects and reference signals. The results indicate that underactuated hands, even with minimalistic sensing and control via visual servoing, can provide a simple and inexpensive solution to allow low-fidelity precision manipulation.

In this paper, we propose a hybrid methodology based on a combination of analytical, numerical and machine learning methods for performing dexterous, in-hand manipulation with simple, adaptive robot hands. A constrained optimization scheme utilizes analytical models that describe the kinematics of adaptive hands and classic conventions for modelling quasistatically the manipulation problem, providing intuition about the problem mechanics. A machine learning (ML) scheme is used in order to split the problem space, deriving task-specific models that account for difficult to model, dynamic phenomena (e.g., slipping). In this respect, the ML scheme: 1) employs the simulation module in order to explore the feasible manipulation paths for a specific hand-object system, 2) feeds the feasible paths to an experimental setup that collects manipulation data in an automated fashion, 3) uses clustering techniques in order to group together similar manipulation trajectories, 4) trains a set of task-specific manipulation models and 5) uses classification techniques in order to trigger a task-specific model based on the user provided task specifications. The efficacy of the proposed methodology is experimentally validated using various adaptive robot hands in 2D and 3D in-hand manipulation tasks.

The past decade has seen great progress in the development of adaptive, low-complexity, underactuated robot hands. An advantage of these hands is that they use under-constrained mechanisms and compliance, which facilitate grasping even under significant object pose uncertainties. However, for many minimal contact grasps such as precision fingertip grasps, these hands tend to move the object after a grasp is secured, to an equilibrium configuration determined by the elasticity of the mechanism and the contact forces exerted through the robot fingertips. In this paper, we present a methodology based on constrained optimization methods for deriving stable, minimal effort grasps for underactuated robot hands and compensating for post-contact, in-hand parasitic object motions. To do so, we compute the imposed object motions for different object shapes and sizes and we synthesize appropriate robot arm trajectories that eliminate them. The approach allows for the computation of these grasps and motions even for hands with complex, flexure-based, compliant members. The effectiveness of the proposed methods is validated using a redundant robot arm (Barrett WAM) and a two fingered, compliant, underactuated robot hand (Yale Open Hand model T42), for a series of simulated and experimental paradigms.

In this paper, we analyze and compare existing human grasp taxonomies and synthesize them into a single new taxonomy (dubbed “The GRASP Taxonomy” after the GRASP project funded by the European Commission). We consider only static and stable grasps performed by one hand. The goal is to extract the largest set of different grasps that were referenced in the literature and arrange them in a systematic way. The taxonomy provides a common terminology to define human hand configurations and is important in many domains such as human-computer interaction and tangible user interfaces where an understanding of the human is basis for a proper interface. Overall, 33 different grasp types are found and arranged into the GRASP taxonomy. Within the taxonomy, grasps are arranged according to 1) opposition type, 2) the virtual finger assignments, 3) type in terms of power, precision, or intermediate grasp, and 4) the position of the thumb. The resulting taxonomy incorporates all grasps found in the reviewed taxonomies that complied with the grasp definition. We also show that due to the nature of the classification, the 33 grasp types might be reduced to a set of 17 more general grasps if only the hand configuration is considered without the object shape/size.

Fingertip-based within-hand manipulation, also called precision manipulation, refers to the repositioning of a grasped object within the workspace of a multifingered robot hand without breaking or changing the contact type between each fingertip and the object. Given a robot hand architecture and a set of assumed contact models, this paper presents a method to perform a gross motion analysis of its precision manipulation capabilities, regardless of the particularities of the object being manipulated. In particular, the technique allows the composition of the displacement manifold of the grasped object relative to the palm of the robot hand to be determined as well as the displacements that can be controlled-useful for high-level design and classification of hand function. The effects of a fingertip contacting a body in this analysis are modeled as kinematic chains composed of passive and resistant revolute joints; what permits the introduction of a general framework for the definition and classification of nonfrictional and frictional contact types. Examples of the application of the proposed method in several architectures of multifingered hands with different contact assumptions are discussed; they illustrate how inappropriate contact conditions may lead to uncontrollable displacements of the grasped object.

Performing dexterous manipulation of unknown objects with robot grippers without using high-fidelity contact sensors, active/sliding surfaces, or a priori workspace exploration is still an open problem in robot manipulation and a necessity for many robotics applications. In this paper we present a two-fingered gripper topology that enables an enhanced predefined in-hand manipulation primitive controlled without knowing the size, shape, or other particulars of the grasped object. The in-hand manipulation behavior, namely, the planar manipulation of the grasped body, is predefined thanks to a simple hybrid low-level control scheme and has an increased range of motion due to the introduction of an elastic pivot joint between the two fingers. Experimental results with a prototype clearly show the advantages and benefits of the proposed concept. Given the generality of the topology and in-hand manipulation principle, researchers and designers working on multiple areas of robotics can benefit from the findings.

The design of finger pads for robotic and prosthetic hands is often overlooked, with relatively simple shapes and mechanical properties typically used. The finger pad geometry and mechanical properties are especially important for within-hand dexterous manipulation, and human finger usage patterns suggest extending robotic finger pad usage onto side surfaces could enable a wider range of manipulation motion. In this work, we propose a novel finger pad design that combines a ridged stiff inner structure with air gaps and a flexible outer skin to facilitate both grasp stability and versatile usage of the finger surface. The air gaps enable objects to displace the outer skin and stably settle between two adjacent ridges. During manipulation, the ridges can also serve as predictable pivot points. Experimental results comparing three ridged finger designs to a conventional solid core design show that the ridged designs consistently outperform the reference solid core design for all objects, in terms of the ability to stably manipulate objects through a large motion range without ejection (losing grip on the object). Designs with larger spaces between ridges performed better overall than designs with closer spacing, showing that larger “wells” allow objects to more stably settle into the space between ridges. We anticipate the novel finger pad designs and the analysis of their behavior will inform future robotic hand designs, especially designs which aim to incorporate side finger usage.

In this paper we investigate effects of the mass, kinematic constraints imposed by the joint, and assistance provided by the spring of a pair of quasi-passive knee exoskeletons on the motion of the human body center of mass during normal walking. The exoskeletons implement a spring in parallel with the knee joint in the weight acceptance phase of gait, and allow free rotation during all other phases. We begin with a brief explanation of the exoskeleton design, which employs a friction-based latching mechanism to engage/disengage a spring in parallel with the knee. Additionally, a pair of joint-less mass replicas of the exoskeletons were used to separately investigate the effects of the exoskeleton added mass and articulation. It was found that the exoskeleton mass is the main contributor to the changes in the motion of the center of mass, with more pronounced fluctuations of the center of mass in the mediolateral direction, while the exoskeleton joint and spring had negligible effects over and above those of the mass. Additionally, the exoskeleton mass and assistance conditions respectively resulted in a non-significant increase and a non-significant decrease in the total mechanical work of the body.

While techniques exist for the rapid prototyping of mechanical and electrical components separately, this paper describes a method where commercial Additive Manufacturing (AM) techniques can be used to concurrently construct the mechanical structure and electronic circuits in a robotic or mechatronic system. The technique involves printing hollow channels within parts that are then filled with a low melting point liquid metal alloy that solidifies upon cooling to form electrical traces. This method is compatible with most conventional fused deposition modeling and stereolithography machines, and requires no modification to an existing printer, though the technique could easily be incorporated into multi-material machines. Three primary considerations are explored using the a commercial fused deposition manufacturing (FDM) process as a testbed: material and manufacturing process parameters, simplified injection fluid mechanics, and automatic part generation using standard printed circuit board software tools. As demonstration of the ability to embed circuit in RP parts, a differential-drive robot is printed, populated with discrete electronic components, and injected to create a fully functional robot.

In this paper we present a methodology for discriminating between different objects using only a single force closure grasp with an underactuated robot hand equipped with force sensors. The technique leverages the benefits of simple, adaptive robot grippers (which can grasp successfully without prior knowledge of the hand or the object model), with an advanced machine learning technique (Random Forests). Unlike prior work in literature, the proposed methodology does not require object exploration, release or re-grasping and works for arbitrary object positions and orientations within the reach of a grasp. A two-fingered compliant, underactuated robot hand is controlled in an open-loop fashion to grasp objects with various shapes, sizes and stiffness. The Random Forests classification technique is used in order to discriminate between different object classes. The feature space used consists only of the actuator positions and the force sensor measurements at two specific time instances of the grasping process. A feature variables importance calculation procedure facilitates the identification of the most crucial features, concluding to the minimum number of sensors required. The efficiency of the proposed method is validated with two experimental paradigms involving two sets of fabricated model objects with different shapes, sizes and stiffness and a set of everyday life objects.

We present the design of simple, centimeter-scale modular actuation units (“Active Cells”) and passive compliant nodes that are electromechanically networked to create macroscopically deformable Modular Active Cell-based Structures (MACROs). Each Active Cell is a single degree-of-freedom linear actuator (a “muscle unit”), consisting of fiberglass end-pieces connecting two strands of Nitinol shape-memory alloy and a passive biasing spring. The Nitinol strands are coiled into a tight spring to increase deformations when activated through resistive heating. In-depth examination of the optimization of Nitinol coils with an antagonistic spring is presented, resulting in large repeatable axial cell strains of up to 25%. The design of these cellular muscle units to obtain maximal repeatable stroke is presented, allowing for the construction of larger networks of cells (MACRO modules, akin to a biological “tissue”) that can be customized to a target application. Finally, experimental demonstration of the construction and actuation of some simple MACRO modules is described.

This paper discusses the design of legged walking robots that are exactly constrained during the stance phase of locomotion. Legged robots with a large number of actuated degrees of freedom, while allowing for the widest range of controllable foot placements, often end up with overconstrained kinematics when in contact with the ground, requiring complex redundant control schemes for effective locomotion. Exactly-constrained robots would be capable of full body mobility while avoiding the weight and complexity costs of fully actuating each joint and would also allow for simpler control schemes. We discuss the constraints and degrees of freedom of a common legged robot kinematic structure and describe strategies for removing redundant constraints. Two major design considerations — architectural singularities and the uniqueness of the ground reaction forces — are discussed along with potential solutions. Finally, a prototype exactly-constrained walking robot is presented as a validation of this design strategy.

While there has been a steady progression of research in robotic and mechatronic systems that utilize nickel titanium alloy (Nitinol) as an actuator, the design of the antagonistic element for the inherently “one-way” technology has not been thoroughly investigated and described. In this paper, we discuss the properties of Nitinol-based shape memory alloy actuators as they relate to the design of passive spring antagonists. We describe the major classes of design goals as they relate to the choice of properties of the antagonistic element, and present techniques for optimizing parallel antagonists through passive linear springs in order to maximize the generally most desirable property of the actuator - the maximal repeatable strain of the antagonist pair.

The proposed research effort explores the development of active cells - simple contractile electromechanical units that can be used as the material basis for larger articulable structures. Each cell, which might be considered a “muscle unit”, consists of a contractile Nitinol SMA core with conductive terminals. Large numbers of these cells might be combined and externally powered to change phase, contracting to either articulate with a large strain or increase the stiffness of the ensemble, depending on the cell design. Unlike traditional work in modular robotics, the approach presented here focuses on cells that have a simplistic design and function, are inexpensive to fabricate, and are eventually scalable to sub-millimeter sizes, working towards our vision of robot structures that can be custom-fabricated from large numbers of general cell units, similar to biological structures.

This paper examines aspects of robot hand performance specific to grasping and perching from an aerial vehicle and shows how various hand design parameters affect performance. Specifically, we consider hand performance when subject to external forces imparted to the hand from carrying a payload or from perching on a fixed item and explore the impact of design and grasp parameters including tendon routing/pulley ratio, object size, and palm size on the performance of both fully and underactuated designs. Our results show that underactuated designs utilizing a single actuator per finger are sufficient in all cases we studied, but that fully actuated designs can perform better for perching applications. Additionally, we find that increasing the palm width improves performance both when perching and grasping, and that a small distal/proximal pulley ratio is beneficial for payload carriage but counterproductive for perching.

Three-dimensional printing technology, also known as additive manufacturing, has shown a significant increase in popularity as the cost of printers comes down and part accuracy and build quality continually improves. To date, the major limitation of the various additive manufacturing techniques is the limited range of print materials and properties, with 3d printed parts unable to be used in most load-bearing applications in robotics and other domains. In this paper, we present a technique for increasing the strength of 3d printed parts while retaining the benefits of the process such as ease and speed of implementation and complex part geometries. By carefully placing voids in the printed parts, which are later filled with higher-strength resins, we can improve the overall part strength and stiffness by up to 45% and 25%, respectively. We show three-point bend testing data comparing solid printed ABS samples with those strengthened through the fill compositing process, as well as examples of 3D printed parts used in robotic applications.

Designing robot hands for dexterous precision manipulation involves many complex tradeoffs in order to optimize hand performance. While many studies focus on overall hand kinematics, far fewer consider tradeoffs in the design of the robotic finger surfaces themselves. Our present work uses 3.8 total hours of precision manipulation from 19 participants to look at the fingertip surfaces used while moving a sphere through as much of the feasible position workspace as possible. Fingertip surface use is estimated by measuring the relative orientation changes between a high-resolution 6DOF sensor mounted on the fingernails of the fingers and in the object being manipulated, indicating to what extent the object has been “rolled” onto the sides of the fingers. The results show significant lateral use of the index and middle fingers, and also show that the side surface of the index finger is used much more in two-finger manipulation than three finger manipulation. The lateral fingertip usage suggests that robot finger designs could also benefit from enabling lateral surface use. The lateral middle finger use also suggests that fingers can be effectively used as passive supports to supply forces in directions that may not be actively controlled. We anticipate these results should be useful especially for robotic and prosthetic hand design, but also in other fields such as rehabilitation or haptic interface design.

In robot hands, precision manipulation, defined as repositioning of a grasped object within the hand workspace without breaking or changing contact, is a fundamental operation for the accomplishment of highly dexterous manipulation tasks. This paper presents a method to characterize the precision manipulation capabilities of a given robot hand regardless of the particularities of the grasped object. The technique allows determining the composition of the displacement manifold (finite motion) of the grasped object relative to the palm of the robot hand and defining the displacements that can actually be controlled by the hand actuators without depending on external factors to the hand. The approach is based on a reduction of the graph of kinematic constraints related to the hand-object system through proper manipulations of the continuous subgroups of displacements generated by the hand joints and contacts. The proposed method is demonstrated through three detailed and constructive examples of common architectures of simplified multi-fingered hands.

In this paper, we present the synthesis and design of a rotary-to-linear continuously variable transmission with the ability to passively change gear ratio as a function of the output load. The primary mechanism involves variable-pitch rollers whose angle changes as a function of the output load due to the compliance properties of their housing. By changing spring stiffness, the relationship between the linear output load and transmission ratio can be tuned to optimize drive motor operating conditions over the entire range of output loads. After laying out the working concept, we show the performance analysis for such a transmission applied to a 6-W DC motor and present an example design analysis for tuning to maximize power output over the entire range of operating conditions. A prototype system was used to measure key parameters such as rolling resistance and lateral slip coefficients and to evaluate the transmission performance in a target application.

Aerial vehicles are difficult to stabilize, especially when acted upon by external forces. A hovering vehicle interacting with objects and surfaces must be robust to contact forces and torques transmitted to the airframe. These produce coupled dynamics that are distinctly different from those of free flight. While external contact is generally avoided, extending aerial robot functionality to include contact with the environment during flight opens up new and useful areas such as perching, object grasping, and manipulation. These mechanics may be modeled as elastic couplings between the aircraft and the ground, represented by springs in R3×SO(3). We show that proportional derivative and proportional integral derivative (PID) attitude and position controllers that stabilize a rotorcraft in free flight will also stabilize the aircraft during contact for a range of contact displacements and stiffnesses. Simulation of the coupled aircraft dynamics demonstrates stable and unstable modes of the system. We find analytical measures that predict the stability of these systems and consider, in particular, the planar system in which the contact point is directly beneath the rotor. We show through explicit solution of the linearized system that the planar dynamics of the object-helicopter system in vertical, horizontal, and pitch motion around equilibrium remain stable, within a range of contact stiffnesses, under unmodified PID attitude control. Flight experiments with a small-scale PID-stabilized helicopter fitted with a compliant gripper for capturing objects affirm our model's stability predictions.

Robotic and prosthetic hand designers are challenged to replicate as much functionality of the human hand as possible, while minimizing cost and any unnecessary complexity. Selecting which aspects of human hand function to emulate can be difficult, especially when little data is available on unstructured human manipulation behavior. The present work analyzes 19 hours of video with over 9000 grasp instances from two housekeepers and two machinists to find small sets of versatile human grasps. A novel grasp span metric is used to evaluate sets of grasps and pick an optimal grasp set which can effectively handle as many different objects as possible. The results show medium wrap and lateral pinch are both important, versatile grasps for basic object handling. The results suggest that three-fingertip precision grasps such as thumb-2 finger, tripod, or lateral tripod can be used to handle dexterous manipulation of a wide range of objects. The recommended grasp sets can help aid difficult design decisions for robotic and prosthetic hands, as well as suggesting important human hand functionality to restore during hand surgery or rehabilitate in an impaired hand.

This paper presents an algorithm for folding a serial revolute chain into a rigid structure of essentially any desired planar shape. The algorithm is fast (linear in the number of links), and the constructed folding plan only requires an actuation method that sequentially folds triangles as the pattern is laid out, maintaining incremental rigidity of the structure during folding.

Commercially available robotic hands are often expensive, customized for specific platforms, and difficult to modify. In this paper, we present the design of an open-source, low-cost, single actuator underactuated hand that can be created through fast and commonly-accessible rapid-prototyping techniques and simple, off-the-shelf components. This project establishes the design of an adaptive, four-finger hand utilizing simple 3D-printed components, compliant flexure joints, and readily obtainable off-the-shelf parts. Modular and adjustable finger designs are provided, giving the user a range of options depending on the intended use of the hand. The design tradeoffs and decisions made to achieve the 3D-printable, compact and lightweight robotic gripper are discussed, as well as a preliminary discussion of the performance differences between the finger designs. The authors intend this work to be the first in a series of open-source designs to be released, and through the contributions of the open-source user community, result in a large number of design modifications and variations available to researchers.

Dexterous, within-hand manipulation, in which an object generally held in the fingertips is manipulated by the fingers, shares many similarities to parallel robot configurations. This paper shows how to apply a mathematical framework commonly used for parallel robots to study the kinetostatic properties of hands manipulating objects using precision grasps, considering compliance and underactuation in the joints, without requiring the use of the grasp matrix. The proposed framework is suitable for any hand, but we focus on underactuated hands. We show how the natural redundancy present in fully-actuated hands can be eliminated using underactuation, leading to simplified non-redundant systems that are easier to control. We primarily focus our efforts on introducing and describing the theoretical framework, and follow this with an example application using a three-fingered underactuated hand. For this example, we define the feasible workspace as the subspace of the kinematic workspace for which the hand can accomplish a grasp, and we study how the compliance, rest angles, and joint coupling in the fingers can be designed to increase the size of this feasible workspace.

In this paper, we demonstrate an underactuated finger design and grasping method for precision grasping and manipulation of small objects. Taking inspiration from the human grasping strategy for picking up objects from a flat surface, we introduce the flip-and-pinch task, in which the hand picks up a thin object by flipping it into a stable configuration between two fingers. Despite the fact that finger motions are not fully constrained by the hand actuators, we demonstrate that the hand and fingers can interact with the table surface to produce a set of constraints that result in a repeatable quasi-static motion trajectory. Even when utilizing only open-loop kinematic playback, this approach is shown to be robust to variation in object size and hand position. Variation of up to 20° in orientation and 10 mm in hand height still result in experimental success rates of 80% or higher. These results suggest that the advantages of underactuated, adaptive robot hands can be carried over from basic grasping tasks to more dexterous tasks.

This paper presents a method for detecting contact on a compliant link utilizing a method to sense changes in the resonant frequency of the link due to external contact. The approach uses an inexpensive accelerometer mounted on or inside the compliant link and a phase locked loop circuit to oscillate the link at its resonant frequency. Using this approach, we are able to reliably sense contact anywhere on the link with a contact force threshold sensitivity of between 0.05 and 0.15 N depending on the contact location.

This video demonstrates the use of an underactuated robotic hand modified for the flip-and-pinch task to pick up thin objects from a table surface. Though well-suited for power-grasping, underactuated hands have difficulty with pinch-grasping and precision motions. We introduce a repeatable and robust method by which an underactuated hand flips thin objects off the table into a stable pinch grasp. We explain why this task is quasi-static and robust for a wide range of object dimensions.

In this paper we demonstrate an underactuated finger design and grasping method for precision grasping and manipulation of relatively small objects. Taking a cue from human manipulation, we introduce the flip-and-pinch task, in which the hand picks up thin objects from a table surface by flipping it into a stable configuration. Despite the fact that finger motions are not fully constrained by the hand actuators, we demonstrate that the hand and fingers can be configured with the table surface to produce a set of constraints that result in a repeatable quasi-static motion trajectory. This approach is shown to be robust for a variety of object sizes, even when utilizing identical open-loop kinematic playback. Experimental results suggest that the advantages of underactuated, adaptive robot hands can be carried over to dexterous, precision tasks as well.

This paper presents a concept for extending the functionality of robot hands so that they can better manipulate objects too small for an enveloping power grasp. Rather than pinching these objects between the fingertips of a hand, a miniature hand is embedded recursively on the end of a finger, enabling a power grasp on a smaller scale. The mechanics of designing such a gripper to operate without adding additional tendons are analyzed within the framework of underactuated elastic mechanisms. A simplified robot hand is demonstrated having a recursive gripper, and the process of picking up a pen and writing using a multi-scale grasp is demonstrated.

Many robotic hands use compliant joints because they provide several advantages when interacting with objects in unknown environments, but they also modify the relation between external and internal forces and vary the reachable workspace. This work proposes a detailed study of how compliant joints modify the statics of hands, from the point of view of parallel manipulators. The chosen mathematical framework clarifies the role of joint compliance and its effect on the manipulator performance. This framework is then used in an example application to quantify the reduction/increase of torque exerted by the active joints due to the influence of the passive compliant ones for a three fingered hand.

This paper investigates the possibility of increasing the robustness of underactuated grasping through the use of variable transmission ratios. We propose a 4-step procedure to investigate and improve the robustness of an underactuated finger on a fixed object. This procedure maximizes the robustness against random force disturbances to the maximum obtainable value under given circumstances. A simulation study is presented that analyzes the disturbance robustness, followed by an experimental study to confirm the effect. The variable transmission ratio is a promising means to increase grasp robustness and has great application potential.

This paper presents a new method to produce computationally efficient models of robots that have planar elastic flexure joints. An accurate, low-dimensional model of large deformation bending is important to precisely describe the configuration of a flexure-jointed manipulator. The new model is based on the assumption that the curvature of a beam in bending is smooth and, thus, can be approximated by low-order polynomials. This produces a description of flexure motion that can be used as a joint model when expressed as a homogeneous transformation between rigid links--essentially a “drop in” replacement for traditional joint models such as screw coordinates and Denavit-Hartenberg conventions. Derivatives of the joint kinematics such as Jacobians and Hessians are accurate and easy to compute. We will show that with only three parameters, this model faithfully reproduces the elastic deformation of a flexure hinge predicted by the continuum model, even for large angles, without requiring numerical integration or many finite elements. The model can also be used to accurately compute the compliance and compressive buckling load of the flexure, as predicted by the continuum model.

We propose a novel approach to study the ability of an underactuated mechanism, or a mechanism that has fewer actuators than degrees of freedom, to passively adapt to environmental constraints. While prior work in underactuated robotic hands has primarily focused on the mechanism's ability to curl its distal degrees of freedom inward even after the proximal degrees of freedom are constrained by contact with the environment, this paper explores the mechanism's adaptability in terms of both motion and force-application capabilities in the presence of external constraints. Specifically, using four different transmissions for a novel singly-actuated linear three degree-of-freedom mechanism, this paper analyzes how the system's ability to reconfigure joints and apply new contact forces varies as a function of the transmission configuration and object geometry. We show that with more extensive re routing of a single actuator to multiple joints, the mechanism exhibits greater motion and force adaptability at the cost of decreased maximum joint travel and contact forces.

The compliance of an underactuated robotic hand, or a robotic hand with fewer actuators than degrees of freedom, is a function of the mechanism type, the design parameters, and the operational control mode. The transmissions used in underactuated mechanisms can be divided into two main classes based on the self adaptive transmission used to route actuation to the various degrees of freedom, namely the single-acting transmission and the double-acting transmission. While both transmission classes can be represented using a kinematic constraint equation that defines the relationship between actuator and joint motion, the main difference between the two transmission classes is that the kinematic constraint is always active in double-acting mechanisms while there are specific combinations of external disturbances and mechanism parmeters that render the constraint inactive in single-acting mechanisms. While previous studies have only explored the performance of underactuated mechanisms with the constraint always active, this paper identifies the benefits for robotic grasping (such as better disturbance rejection) that arise when the constraint becomes inactive in single-acting mechanisms.

Underactuated fingers have been extensively studied and optimized in order to achieve better grasp performance in terms of object acquisition and stability. However, little work has been done related to the coupling mechanisms between the fingers and their effects on grasp performance. This paper presents a novel method of underactuated finger coupling that utilizes friction and allows for increased stability and adaptability of robotic grippers. We show that variable friction within the coupling element can help the system maintain kinematic form closure while not affecting non-closure forces during grasp acquisition. A proof of concept prototype demonstrates the increased stability of objects within the grasp as compared to traditional coupling mechanisms.

In this paper we show that it is possible to design underactuated robot hands capable of performing dexterous manipulation tasks, despite the fact that the motion of an underactuated hand is not fully constrained by its actuators. If a robot has elastic elements at its joints, then the velocity of the actuators can be mapped onto the velocity of the grasped object using elastic averaging. This mapping can be used to compute classical measures of manipulability for an underactuated hand. We also demonstrate that holonomically constrained grasps can be analyzed to determine the manifold of stable object configurations that can be reached from some initial grasp. This is especially useful for planar manipulation operations, such as twisting a knob or precision positioning. A prototype two-fingered planar underactuated hand is introduced, having the ability to stably grasp and manipulate objects within the hand.

This paper reports recent research efforts to advance the functionality of Unmanned Aerial Vehicles (UAVs) beyond passive observation to active interaction with and manipulation of objects. The archetypical aerial manipulation task - grasping objects during flight - is difficult due to the unstable dynamics of rotorcraft and coupled object-aircraft motion. In this paper, we analyze key challenges encountered when lifting a grasped object and transitioning into laden free-flight. We demonstrate that dynamic load disturbances introduced by the load mass will be rejected by a helicopter with PID flight control. We determine stability bounds in which the changing mass-inertia parameters of the system due to the grasped object will not destabilize this flight controller. The conditions under which transient partial contact mechanics of objects resting on a surface will not induce instability are identified. We demonstrate grasping and retrieval of a variety of objects while hovering, without touching the ground, using the Yale Aerial Manipulator testbed.

This video demonstrates a helicopter Unmanned Aerial Vehicle (UAV) research platform for grasping objects while in flight. Typically, helicopters avoid interacting with objects in their surroundings due to the unstable flight dynamics of rotorcraft and coupled mechanics encountered during contact. We introduce the Yale Aerial Manipulator and demonstrate stable grasping of a range of objects both when landed and while hovering. We discuss the platform's underactuated gripper and its contribution to aircraft stability while grasping.

This paper presents a study on the usage frequency of different grasp types throughout the daily functions of a professional house maid and a machinist. Subjects wore a head-mounted camera that recorded their hand usage during their daily work activities. This video was then analyzed, recording grasp type and associated time stamps, as well as information related to the task and object. The results show that nearly 80% of the time the house maid used just six grasps and the machinist used nine. This data, in conjunction with established grasp taxonomies, will enable a better understanding of how people utilize different grasps to accomplish tasks throughout the day, as well as inform the design of robotic and prosthetic hands.

While underactuated mechanisms have become popular in robot-hand designs because of their passive adaptability, existing systems utilize only one actuator to produce motion in the multiple degrees of freedom in the serial chain of each finger. In this paper, we explore how the performance of an underactuated serial link chain changes as more actuators are added. The fundamental question of what extra capability an additional actuator provides to an underactuated system and how best to implement it has not yet been quantified in the literature. Using a simple linear underactuated mechanism, we show that the performance of a single-actuator system (measured as the average number of contacts made with the environment) quickly plateaus as the number of degrees of freedom of the mechanism is increased. Also, we show that as the number of actuators is increased, the system's passive adaptability improves as the mechanism implementation spreads the actuators across the joints.

Sheet hinges, thin flexures that are rigid in the plane but which can bend freely, are common in stamped and lithographically manufactured devices. The behavior of these machine elements as joints in a robot is difficult to model because they are two-dimensional continuum elastic bodies that admit three-dimensional motion and twisting. This paper presents a parametric modeling technique that can be used to accurately predict elastic behavior of sheet hinges in three dimensions. Parameterized backbone curves can be used to represent ruled surface bending in a fashion that implicitly accounts for some of the complex boundary conditions imposed on typical sheet hinges. Approximate methods of integrating the non-commutative equations defining the sheet hinge backbone curves will be discussed, demonstrating acceptable trade-offs between accuracy and representational simplicity in overall model performance.

A hovering vehicle interacting with objects and surfaces must be robust to contact forces and torques transmitted to the airframe, which produce coupled dynamics distinctly different from those of free flight. These mechanics may be modeled as elastic couplings between the aircraft and the ground, represented by a 6-DOF spring in ℝ3×SO(3).We show that Proportional Derivative attitude and position controllers that stabilize a rotorcraft in free flight will also stabilize the aircraft during contact for a range of contact displacements and stiffnesses. Simulation of the coupled aircraft dynamics demonstrates stable and unstable modes of the system.

This paper presents a number of concepts related to benchmarking and evaluation of grasping and manipulation. A set of “Objects of Daily Living” based on a review of common domestic objects for manipulation as identified from sources in the literature is put forward, along with the physical properties of sample objects in those categories. Next, an experimental evaluation of the coefficient of static friction between these objects and a number of common household surfaces is performed. A key failure mode in unstructured object grasping occurs when the manipulator applies large contact forces that move the object out of grasp range. These results therefore give insight into the likelihood of a target object remaining in place to be successfully grasped in the presence of contact forces from the robot arm. This paper also presents a new classification of the Activities of Daily Living (ADLs), putting forth a standard categorization for the application of robotics in human environments. These topics and results have a number of uses related to benchmarking and performance evaluation in robotic manipulation, assistive technology, and prosthetics.

In this paper we describe the design and preliminary evaluation of an energetically-autonomous powered knee exoskeleton to facilitate running. The device consists of a knee brace in which a motorized mechanism actively places and removes a spring in parallel with the knee joint. This mechanism is controlled such that the spring is in parallel with the knee joint from approximately heel-strike to toe-off, and is removed from this state during the swing phase of running. In this way, the spring is intended to store energy at heel-strike which is then released when the heel leaves the ground, reducing the effort required by the quadriceps to exert this energy, thereby reducing the metabolic cost of running.

In the nearly six decades since researchers began to explore methods of creating them, exoskeletons have progressed from the stuff of science fiction to nearly commercialized products. While there are still many challenges associated with exoskeleton development that have yet to be perfected, the advances in the field have been enormous. In this paper, we review the history and discuss the state-of-the-art of lower limb exoskeletons and active orthoses. We provide a design overview of hardware, actuation, sensory, and control systems for most of the devices that have been described in the literature, and end with a discussion of the major advances that have been made and hurdles yet to be overcome.

The inherent uncertainty associated with unstructured grasping tasks makes establishing a successful grasp difficult. Traditional approaches to this problem involve hands that are complex, fragile, require elaborate sensor suites, and are difficult to control. In this paper, we demonstrate a novel autonomous grasping system that is both simple and robust. The four-fingered hand is driven by a single actuator, yet can grasp objects spanning a wide range of size, shape, and mass. The hand is constructed using polymer-based shape deposition manufacturing, with joints formed by elastomeric flexures and actuator and sensor components embedded in tough rigid polymers. The hand has superior robustness properties, able to withstand large impacts without damage and capable of grasping objects in the presence of large positioning errors. We present experimental results showing that the hand mounted on a three degree of freedom manipulator arm can reliably grasp 5 cm-scale objects in the presence of positioning error of up to 100% of the object size and 10 cm-scale objects in the presence of positioning error of up to 33% of the object size, while keeping acquisition contact forces low.

This paper examines the role of grasper compliance and kinematic configuration in unstructured environments, where object size and location may not be well known. A grasper consisting of two two-link planar fingers with compliant revolute joints was simulated as it passively deflects during contact with a target object. The kinematic configuration and joint stiffness values of the grasper were varied in order to maximize grasper workspace for a wide range of target object size. The results show a near-optimal result around the spring-rest angles of 25 and 45 degrees for the base and intermediate joints, respectively, when the joint stiffness ratio (base/intermediate) was small.