The presentation of a robot's capability and identity directly influences a human collaborator's perception and implicit trust in the robot. Unlike humans, a physical robot can simultaneously present different identities and have them reside and control different parts of the robot. This paper presents a novel study that investigates how users perceive a robot where different robot control domains (head and gripper) are presented as independent robots. We conducted a mixed design study where participants experienced one of three presentations: a single robot, two agents with shared full control (co-embodiment), or two agents with split control across robot control domains (split-embodiment). Participants underwent three distinct tasks -- a mundane data entry task where the robot provides motivational support, an individual sorting task with isolated robot failures, and a collaborative arrangement task where the robot causes a failure that directly affects the human participant. Participants perceived the robot as residing in the different control domains and were able to associate robot failure with different identities. This work signals how future robots can leverage different embodiment configurations to obtain the benefit of multiple robots within a single body.

Object 6D pose estimation is a critical challenge in robotics, particularly for manipulation tasks. While prior research combining visual and tactile (visuotactile) information has shown promise, these approaches often struggle with generalization due to the limited availability of visuotactile data. In this paper, we introduce ViTa-Zero, a zero-shot visuotactile pose estimation framework. Our key innovation lies in leveraging a visual model as its backbone and performing feasibility checking and test-time optimization based on physical constraints derived from tactile and proprioceptive observations. Specifically, we model the gripper-object interaction as a spring-mass system, where tactile sensors induce attractive forces, and proprioception generates repulsive forces. We validate our framework through experiments on a real-world robot setup, demonstrating its effectiveness across representative visual backbones and manipulation scenarios, including grasping, object picking, and bimanual handover. Compared to the visual models, our approach overcomes some drastic failure modes while tracking the in-hand object pose. In our experiments, our approach shows an average increase of 55% in AUC of ADD-S and 60% in ADD, along with an 80% lower position error compared to FoundationPose.

Traditional approaches to motion modeling for skid-steer robots struggle to capture nonlinear tire-terrain dynamics, especially during high-speed maneuvers. In this paper, we tackle such nonlinearities by enhancing a dynamic unicycle model with Gaussian Process (GP) regression outputs. This enables us to develop an adaptive, uncertainty-informed navigation formulation. We solve the resultant stochastic optimal control problem using a chance-constrained Model Predictive Path Integral (MPPI) control method. This approach formulates obstacle avoidance and path-following as chance constraints, accounting for residual uncertainties from the GP to ensure safety and reliability in control. Leveraging GPU acceleration, we efficiently manage the non-convex nature of the problem, ensuring real-time performance. Our approach unifies path-following and obstacle avoidance across different terrains, unlike prior works which typically focus on one or the other. We compare our GP-MPPI method against unicycle and data-driven kinematic models within the MPPI framework. In simulations, our approach shows superior tracking accuracy and obstacle avoidance. We further validate our approach through hardware experiments on a skid-steer robot platform, demonstrating its effectiveness in high-speed navigation. The GPU implementation of the proposed method and supplementary video footage are available at https://stochasticmppi.github.io.

Firefighters operate in hazardous environments with limited ergonomic support, often leading to significant physical strain. While robotics research has explored drones and quadrupeds for firefighting assistance, exoskeletons remain underutilized. This study evaluates the effects of the BackX passive exoskeleton during firefighter search and rescue tasks. Five professional firefighters performed rescue and equipment carry tasks with and without the exoskeleton, while physiological metrics were recorded using the COSMED K5 metabolic analyzer. Results showed a reduction in cardiovascular strain and anaerobic demand when using the exoskeleton; however, energy expenditure increased, likely due to restricted movement and inefficiencies. Post-task surveys indicated reduced perceived exertion and fatigue. These findings suggest that passive exoskeletons may alleviate physical demands but require further development to improve energy efficiency and usability in dynamic emergency scenarios. Continued research is necessary to optimize exoskeleton design for fire service applications and to assess long-term operational benefits.

The seafood processing industry provides fertile ground for robotics to impact the future-of-work from multiple perspectives including productivity, worker safety, and quality of work life. The robotics research challenge in this domain is the realization of flexible and reliable manipulation of soft, deformable, slippery, spiky and scaly objects. In this paper, we propose a novel robot end effector, called HASHI, that employs chopstick-like appendages for precise and dexterous manipulation. This gripper is capable of in-hand manipulation by rotating its two constituent sticks relative to each other and offers control of objects in all three axes of rotation by imitating human use of chopsticks. HASHI delicately positions and orients food through embedded 6-axis force-torque sensors. We derive and validate the kinematic model for HASHI, as well as demonstrate grip force and torque readings from the sensorization of each chopstick. We also evaluate the versatility of HASHI through grasping trials of a variety of real and simulated food items with varying geometry, weight, and firmness.

Skid-Steer Wheeled Mobile Robots (SSWMRs) are increasingly being used for off-road autonomy applications. When turning at high speeds, these robots tend to undergo significant skidding and slipping. In this work, using Gaussian Process Regression (GPR) and Sigma-Point Transforms, we estimate the non-linear effects of tire-terrain interaction on robot velocities in a probabilistic fashion. Using the mean estimates from GPR, we propose a data-driven dynamic motion model that is more accurate at predicting future robot poses than conventional kinematic motion models. By efficiently solving a convex optimization problem based on the history of past robot motion, the GPR augmented motion model generalizes to previously unseen terrain conditions. The output distribution from the proposed motion model can be used for local motion planning approaches, such as stochastic model predictive control, leveraging model uncertainty to make safe decisions. We validate our work on a benchmark real-world multi-terrain SSWMR dataset. Our results show that the model generalizes to three different terrains while significantly reducing errors in linear and angular motion predictions. As shown in the attached video, we perform a separate set of experiments on a physical robot to demonstrate the robustness of the proposed algorithm.

Visual navigation has received significant attention recently. Most of the prior works focus on predicting navigation actions based on semantic features extracted from visual encoders. However, these approaches often rely on large datasets and exhibit limited generalizability. In contrast, our approach draws inspiration from traditional navigation planners that operate on geometric representations, such as occupancy maps. We propose StereoNavNet (SNN), a novel visual navigation approach employing a modular learning framework comprising perception and policy modules. Within the perception module, we estimate an auxiliary 3D voxel occupancy grid from stereo RGB images and extract geometric features from it. These features, along with user-defined goals, are utilized by the policy module to predict navigation actions. Through extensive empirical evaluation, we demonstrate that SNN outperforms baseline approaches in terms of success rates, success weighted by path length, and navigation error. Furthermore, SNN exhibits better generalizability, characterized by maintaining leading performance when navigating across previously unseen environments.

Shared control can ease and enhance a human operator’s ability to teleoperate robots, particularly for intricate tasks demanding fine control over multiple degrees of freedom. However, the arbitration process dictating how much autonomous assistance to administer in shared control can confuse novice operators and impede their understanding of the robot’s behavior. To overcome these adverse side-effects, we propose a novel formulation of shared control that enables operators to tailor the arbitration to their unique capabilities and preferences. Unlike prior approaches to "customizable" shared control where users could indirectly modify the latent parameters of the arbitration function by issuing a feedback command, we instead make these parameters observable and directly editable via a virtual reality (VR) interface. We present our user-customizable shared control method for a teleoperation task in SE(3), known as the buzz wire game. A user study is conducted with participants teleoperating a robotic arm in VR to complete the game. The experiment spanned two weeks per subject to investigate longitudinal trends. Our findings reveal that users allowed to interactively tune the arbitration parameters across trials generalize well to adaptations in the task, exhibiting improvements in precision and fluency over direct teleoperation and conventional shared control.

Conventional bidirectional conveyance platforms use a flat translating belt or a series of spinning wheels or rollers to apply a shear force to payloads to move them. Wheel/roller-based conveyors in particular cannot double as a worktop when idle, do not support collision-free multi-object manipulation by default, and are not optimized to move objects that are either slippery or pliable—let alone both. This paper introduces a Voxel-Enabled Robotic Assistant (VERA), a network of intelligent table "partitions" whose topologically dynamic worktops enable omnidirectional conveyance; each partition is composed of a 2D array of "quadrants," axisymmetric modules that can be hot-swapped for maintenance or repairs; each quadrant contains a 2D array of "cells," unitary robotic submodules; each cell houses an independently controllable "voxel," the motorized rotary element that conveys an overhead object. The efficacy of a VERA prototype was determined by evaluating waypoint error as a range of payloads were maneuvered between trajectory waypoints. By conveying both pliable and rigid payloads having slippery textures, the faceted voxels outperformed those augmented to mimic the circular-profiled wheels/rollers of competitor systems. VERA also successfully performed collision-free multi-object planar manipulations planned by its pathfinding algorithm. In light of these results, VERA emerges as a promising material handling platform for use in "Future of Work" settings as the need for multi-purpose collaborative industrial robots continues to grow.

Near Infrared (NIR) spectroscopy is widely used in industrial quality control and automation to test the purity and grade of items. In this research, we propose a novel sensorized end effector and acquisition strategy to capture spectral signatures from objects and register them with a 3D point cloud. Our methodology first takes a 3D scan of an object generated by a time-of-flight depth camera and decomposes the object into a series of planned viewpoints covering the surface. We generate motion plans for a robot manipulator and end-effector to visit these viewpoints while maintaining a fixed distance and surface normal. This process is enabled by the spherical motion of the end-effector and ensures maximal spectral signal quality. By continuously acquiring surface reflectance values as the end-effector scans the target object, the autonomous system develops a four-dimensional model of the target object: position in an R3coordinate frame, and a reflectance vector denoting the associated spectral signature. We demonstrate this system in building spectral-spatial object profiles of increasingly complex geometries. We show the proposed system and spectral acquisition planning produce more consistent spectral signals than naïve point scanning strategies. Our work represents a significant step towards high-resolution spectral-spatial sensor fusion for automated quality assessment.

Human-in-the-loop robot teleoperation interfaces enable operators to control robots to complete complex tasks, as seen by the success of teams in the DARPA Robotics Challenge (DRC). In this work, we compare two human-in-the-loop planning interfaces, a 2D keyboard and mouse (KBM) interface modeled after those used in the DRC and a 3D virtual reality (VR) interface, for teleoperating a robot to perform navigation and manipulation tasks. In our study, we investigated operator performance, and cognitive workload while using the interface, as well as the perceived usability of each. We found that participants had better performance in both task types when using the KBM interface, however they experienced fewer collisions between the robot and the world in the VR interface. Given these findings, we recommend utilizing a KBM interface in low-risk situations where task performance is the primary factor. In high-risk scenarios, where collisions can be detrimental, we recommend using VR. With this work we aim to contribute to building effective and intuitive interfaces for human-in-the-loop planning to allow robots to complete complex tasks in challenging environments.

Liquids and granular media are pervasive throughout human environments. Their free-flowing nature causes people to constrain them into containers. We do so with thousands of different types of containers made out of different materials with varying sizes, shapes, and colors. In this work, we present a state-of-the-art sensing technique for robots to perceive what liquid is inside of an unknown container. We do so by integrating Visible to Near Infrared (VNIR) reflectance spectroscopy into a robot's end effector. We introduce a hierarchical model for inferring the material classes of both containers and internal contents given spectral measurements from two integrated spectrometers. To train these inference models, we capture and open source a dataset of spectral measurements from over 180 different combinations of containers and liquids. Our technique demonstrates over 85% accuracy in identifying 13 different liquids and granular media contained within 13 different containers. The sensitivity of our spectral readings allow our model to also identify the material composition of the containers themselves with 96% accuracy. Overall, VNIR spectroscopy presents a promising method to give household robots a general-purpose ability to infer the liquids inside of containers, without needing to open or manipulate the containers.

Obstacle detection is a safety-critical problem in robot navigation, where stereo matching is a popular vision-based approach. While deep neural networks have shown impressive results in computer vision, most of the previous obstacle detection works only leverage traditional stereo matching techniques to meet the computational constraints for real-time feedback. This paper proposes a computationally efficient method that employs a deep neural network to detect occupancy from stereo images directly. Instead of learning the point cloud correspondence from the stereo data, our approach extracts the compact obstacle distribution based on volumetric representations. In addition, we prune the computation of safety irrelevant spaces in a coarse-to-fine manner based on octrees generated by the decoder. As a result, we achieve real-time performance on the onboard computer (NVIDIA Jetson TX2). Our approach detects obstacles accurately in the range of 32 meters and achieves better IoU (Intersection over Union) and CD (Chamfer Distance) scores with only 2% of the computation cost of the state-of-the-art stereo model. Furthermore, we validate our method's robustness and real-world feasibility through autonomous navigation experiments with a real robot. Hence, our work contributes toward closing the gap between the stereo-based system in robot perception and state-of-the-art stereo models in computer vision. To counter the scarcity of high-quality real-world indoor stereo datasets, we collect a 1.36 hours stereo dataset with a mobile robot which is used to fine-tune our model. The dataset, the code, and further details including additional visualizations are available at https://lhy.xyz/stereovoxelnet/.

This paper reports on Team Northeastern's Avatar system for telepresence, and our holistic approach to meet the ANA Avatar XPRIZE Final testing task requirements. The system features a dual-arm configuration with hydraulically actuated glove-gripper pair for haptic force feedback. Our proposed Avatar system was evaluated in the ANA Avatar XPRIZE Finals and completed all 10 tasks, scored 14.5 points out of 15.0, and received the 3rd Place Award. We provide the details of improvements over our first generation Avatar, covering manipulation, perception, locomotion, power, network, and controller design. We also extensively discuss the major lessons learned during our participation in the competition.

Humans can perform seamless object handovers with little to no effort. These handovers are characterized by an early movement onset that anticipates the handover location and a smooth velocity profile with minimal trajectory corrections. Replicating these characteristics in an object handover task between humans and robots presents a significant modeling challenge. In this paper we implement a Gaussian Process prediction model to serve as a robotic surrogate of human inference, and investigate how this model affects the kinematics of a human giver handing an object to the robot. Additionally, we analyze how the resulting robot kinematics compare to those of a human, and gauge human comfort through subjective reporting. Human giver kinematics during human-robot handover compared closely to human-human giver kinematics with respect to movement speed, movement timing, movement smoothness, and handover distance. Notable differences were observed in reach time and receiver peak transport velocity. When asked how well four attributes of their human-robot handovers (receiver competence, handover comfort, handover naturalness, handover safety) compared to those attributes in human-human handovers, subjects gave mean scores ranging from 4.43 (naturalness) to 5.13 (safety) on a 7 point Likert scale.

This paper presents a Deep Reinforcement Learning based navigation approach in which we define the occu-pancy observations as heuristic evaluations of motion primitives, rather than using raw sensor data. Our method enables fast mapping of the occupancy data, generated by multi-sensor fusion, into trajectory values in 3D workspace. The computationally efficient trajectory evaluation allows dense sampling of the action space. We utilize our occupancy observations in different data structures to analyze their effects on both training process and navigation performance. We train and test our methodology on two different robots within challenging physics-based simulation environments including static and dy-namic obstacles. We benchmark our occupancy representations with other conventional data structures from state-of-the-art methods. The trained navigation policies are also validated successfully with physical robots in dynamic environments. The results show that our method not only decreases the required training time but also improves the navigation performance as compared to other occupancy representations. The open-source implementation of our work and all related info are available at https://github.com/RIVeR-Lab/tentabot.

Terrain classification is a challenging task for robots operating in unstructured environments. Existing classification methods make simplifying assumptions, such as a reduced number of classes, clearly segmentable roads, or good lighting conditions, and focus primarily on one sensor type. These assumptions do not translate well to off-road vehicles, which operate in varying terrain conditions. To provide mobile robots with the capability to identify the terrain being traversed and avoid undesirable surface types, we propose a multimodal sensor suite capable of classifying different terrains. We capture high resolution macro images of surface texture, spectral reflectance curves, and localization data from a 9 degrees of freedom (DOF) inertial measurement unit (IMU) on 11 different terrains at different times of day. Using this dataset, we train individual neural networks on each of the modalities, and then combine their outputs in a fusion network. The fused network achieved an accuracy of 99.98% percent on the test set, exceeding the results of the best individual network component by 0.98%. We conclude that a combination of visual, spectral, and IMU data provides meaningful improvement over state of the art in terrain classification approaches. The data created for this research is available at https://github.com/RIVeR-Lab/vast_data.

There has been a drastic shift to remote interaction for professional, industrial and personal interactions. Improving the overall quality of these interactions by removing any sense of distance between the users is the ultimate goal. Video conferencing has been widely adopted as an improvement to audio-only interactions. Having added visuals to audio communication, the next frontier is to add physical interaction to this remote communication. In this paper, we present an avatar system with the aim of tackling these necessities. The proposed system includes both hardware and software designs to ensure a real-time telemanipulation experience with tactile force feedback. We present a coupled hydrostatic actuated gripper and glove with high system bandwidth to reduce the inherent latency of the mechanical system. To account for latency over the network, the wave variable based method is adopted to maintain the stability of the closed-loop gripper control even under hundreds of milliseconds of delay. A bidirectional audiovisual communication system comprised of off-the-shelf hardware and software is incorporated to allow realtime conversation between the operator and the recipient for collaborative tasks. the proposed system has been validated in lab experiments and the global ana avatar xprize challenge semifinal.

Collaborative robots that physically interact with humans in an ergonomic and safe manner are essential to the future of industry. A common task across many industrial applications is robot-to-human handover, in which the location of object exchange is vital in cultivating a seamless interaction. Most prior work on computing these exchange locations aims to adjust human posture towards a better ergonomic state during a single handover. This procedure typically involves the robot estimating the human’s biomechanical properties, e.g. center of mass and base of support, before determining an optimal handover location according to some ergonomics assessment scale. In a similar vein, we compare two methodologies for object handover, whereby the handover location is computed to either "assist" or "stimulate" the human receiver. Unlike existing approaches, we posit that improvements in human posture can be derived by stimulating the receiver’s movement dynamics to facilitate posture variability, rather than constrain or stabilize it. To compare methodologies, we conduct a within-subjects study where participants perform 78 object handovers with a collaborative robot architecture. Our ndings indicate an improvement in ergonomics scores for the "stimulating" approach, hinting at the importance of productive inconvenience in long-term robot-to-human handover.

Origami structures can transform their form and function by changing the direction of their folds. This reconfiguration can enable multifunctional robots, but doing so requires a fast, robust, and repeatable actuation method. In this article, we present an origami gripper that uses dynamic transformation to change its kinematic behavior in less than a second. We characterize individual vertices to show that the transformation is predictable and repeatable for different designs and orientations. We then apply it to a multivertex template that is capable of a wide range of shapes and motion patterns, indicating that transformation can be generalized to complex and functional machines. To demonstrate this, we built a transforming origami gripper on a robotic arm to pick up multiple objects. Demonstrations show that the gripper can quickly reconfigure between three different grasping modes and has sufficient stiffness to engage with and lift multiple objects with distinct geometries.

Predicting human trajectories is an important component of autonomous moving platforms, such as social robots and self-driving cars. Human trajectories are affected by both the physical features of the environment and social interactions with other humans. Despite recent surge of studies on human path prediction, most works focus on static scene information, therefore, cannot leverage the rich dynamic visual information of the scene. In this work, we propose Introvert, a model which predicts human path based on his/her observed trajectory and the dynamic scene context, captured via a conditional 3D visual attention mechanism working on the input video. Introvert infers both environment constraints and social interactions through observing the dynamic scene instead of communicating with other humans, hence, its computational cost is independent of how crowded the surrounding of a target human is. In addition, to focus on relevant interactions and constraints for each human, Introvert conditions its 3D attention model on the observed trajectory of the target human to extract and focus on relevant spatiotemporal primitives. Our experiments on five publicly available datasets show that the Introvert improves the prediction errors of the state of the art.

State-of-the-art human-in-the-loop robot grasping is hugely suffered by Electromyography (EMG) inference robustness issues. As a workaround, researchers have been looking into integrating EMG with other signals, often in an ad hoc manner. In this paper, we are presenting a method for end-to-end training of a policy for human-in-the-loop robot grasping on real reaching trajectories. For this purpose we use Reinforcement Learning (RL) and Imitation Learning (IL) in DEXTRON (DEXTerity enviRONment), a stochastic simulation environment with real human trajectories that are augmented and selected using a Monte Carlo (MC) simulation method. We also offer a success model which once trained on the expert policy data and the RL policy roll-out transitions, can provide transparency to how the deep policy works and when it is probably going to fail.

Recent work on robot learning with visual observations has shown great success in solving many manipulation tasks. While visual observations contain rich information about the environment and the robot, they can be unreliable in the presence of visual noise or occlusions. In these cases, we can leverage tactile observations generated by the interaction between the robot and the environment. In this paper, we propose a framework for learning manipulation policies that fuse visual and tactile feedback. The control problems considered in this work are to localize a gripper with respect to the environment image and navigate to desired states. Our method uses a learned Bayes filter to estimate the state of a gripper by conditioning the tactile observations on the environment image. We use deep reinforcement learning for solving the localization and navigation problems provided with the belief of the gripper’s state and the environment image. We compare our method against two baselines where the agent uses tactile observation directly with a recurrent neural network or uses a point estimate of the state instead of the full belief state. We also transfer the policies to the real world and validate them on a physical robot.

Extreme environments, such as search and rescue missions, defusing bombs, or exploring extraterrestrial planets, are unsafe environments for humans to be in. Robots enable humans to explore and interact in these environments through remote presence and teleoperation and virtual reality provides a medium to create immersive and easy-to-use teleoperation interfaces. However, current virtual reality interfaces are still very limited in their capabilities. In this work, we aim to advance robot teleoperation virtual reality interfaces by developing an environment reconstruction methodology capable of recognizing objects in a robot’s environment and rendering high fidelity models inside a virtual reality headset. We compare our proposed environment reconstruction method against traditional point cloud streaming by having operators plan waypoint trajectories to accomplish a pick-and-place task. Overall, our results show that users find our environment reconstruction method more usable and less cognitive work compared to raw point cloud streaming.

We present a contact-implicit trajectory optimization framework that can plan contact-interaction trajectories for different robot architectures and tasks using a trivial initial guess and without requiring any parameter tuning. This is achieved by using a relaxed contact model along with an automatic penalty adjustment loop for suppressing the relaxation. Moreover, the structure of the problem enables us to exploit the contact information implied by the use of relaxation in the previous iteration, such that the solution is explicitly improved with little computational overhead. We test the proposed approach in simulation experiments for non-prehensile manipulation using a 7-DOF arm and a mobile robot and for planar locomotion using a humanoid-like robot in zero gravity. The results demonstrate that our method provides an out-of-the-box solution with good performance for a wide range of applications.

Localizing and tracking the pose of robotic grippers are necessary skills for manipulation tasks. However, the manipulators with imprecise kinematic models (e.g. low-cost arms) or manipulators with unknown world coordinates (e.g. poor camera-arm calibration) cannot locate the gripper with respect to the world. In these circumstances, we can leverage tactile feedback between the gripper and the environment. In this paper, we present learnable Bayes filter models that can localize robotic grippers using tactile feedback. We propose a novel observation model that conditions the tactile feedback on visual maps of the environment along with a motion model to recursively estimate the gripper's location. Our models are trained in simulation with self-supervision and transferred to the real world. Our method is evaluated on a tabletop localization task in which the gripper interacts with objects. We report results in simulation and on a real robot, generalizing over different sizes, shapes, and configurations of the objects.

Avoiding obstacles in the perceived world has been the classical approach to autonomous mobile robot navigation. However, this usually leads to unnatural and inefficient motions that significantly differ from the way humans move in tight and dynamic spaces, as we do not refrain interacting with the environment around us when necessary. Inspired by this observation, we propose a framework for autonomous robot navigation among movable obstacles (NAMO) that is based on the theory of affordances and contact-implicit motion planning. We consider a realistic scenario in which a mobile service robot negotiates unknown obstacles in the environment while navigating to a goal state. An affordance extraction procedure is performed for novel obstacles to detect their movability, and a contact-implicit trajectory optimization method is used to enable the robot to interact with movable obstacles to improve the task performance or to complete an otherwise infeasible task. We demonstrate the performance of the proposed framework by hardware experiments with Toyota's Human Support Robot.

This paper presents a new method of maximizing the free space for a robot operating in a constrained environment under operator supervision. The objective is to make the resulting trajectories more robust to operator commands and/or changes in the environment. To represent the volume of free space, the constrained manipulability polytopes are used. These polytopes embed the distance to obstacles, the distance to joint limits and the distance to singular configurations. The volume of the resulting Cartesian polyhedron is used in an optimization-based motion planner to create the trajectories. Additionally, we show how fast collision-free inverse kinematic solutions can be obtained by exploiting the pre-computed inequality constraints. The proposed algorithm is validated in simulation and experimentally.

In this paper, we propose a contact-implicit trajectory optimization (CITO) method based on a variable smooth contact model (VSCM) and successive convexification (SCvx). The VSCM facilitates the convergence of gradient-based optimization without compromising physical fidelity. On the other hand, the proposed SCvx-based approach combines the advantages of direct and shooting methods for CITO. For evaluations, we consider non-prehensile manipulation tasks. The proposed method is compared to a version based on iterative linear quadratic regulator (iLQR) on a planar example. The results demonstrate that both methods can find physically-consistent motions that complete the tasks without a meaningful initial guess owing to the VSCM. The proposed SCvx-based method outperforms the iLQR-based method in terms of convergence, computation time, and the quality of motions found. Finally, the proposed SCvx-based method is tested on a standard robot platform and shown to perform efficiently for a real-world application.

In this paper, we analyze the effects of contact models on contact-implicit trajectory optimization for manipulation. We consider three different approaches: (1)a contact model that is based on complementarity constraints, (2)a smooth contact model, and our proposed method (3) a variable smooth contact model. We compare these models in simulation in terms of physical accuracy, quality of motions, and computation time. In each case, the optimization process is initialized by setting all torque variables to zero, namely, without a meaningful initial guess. For simulations, we consider a pushing task with varying complexity for a 7 degrees-of-freedom robot arm. Our results demonstrate that the optimization based on the proposed variable smooth contact model provides a good trade-off between the physical fidelity and quality of motions at the cost of increased computation time.

In this paper, we analyze systems with low throughput human-machine interfaces (such as a brain-computer interface, single switch interface) from the controls perspective. We develop some principles for performance improvement in such systems based on the parallelization of inference and robot motion. The proposed principles are used to design a novel shared position control to navigate a circular massless holonomic robot in a known environment. The system is implemented in simulation and integrated with a real robotic wheelchair. Robot experiments demonstrated the viability of the proposed navigation method in various modes of operation.

Path planning on a 2D-grid is a well-studied problem in robotics. It usually involves searching for a shortest path between two vertices on a grid. Single-source path planning is a modified problem which asks to find distances from a given point to all other points on the map. A high-performance algorithm for single-source any-angle path planning on a grid that we named CWave is proposed in this work. “Any-angle” attribute of a path planning algorithm implies that such algorithm can find paths which may include any angle segments, as opposed to standard A* on an 8-connected graph, the path can turn with 45°-increments only. The key idea of the presented algorithm is that it does not represent the grid as a graph and uses discrete geometric primitives to define the wave front. In its purest form, CWave requires for computation only integer arithmetics and multiplication by two, but can accumulate the distance error at turning points. A modified version of CWave with minimal usage of floating-point calculations is also developed. It allows to eliminate any accumulative errors which is proven mathematically and experimentally on several maps. The performance of the algorithm on three maps is demonstrated to be significantly faster than that of Theta*, Lazy Theta* and Field A* adapted for single-source planning. The limitations of the current implementations of the algorithm as well as potential improvements are discussed.

This paper introduces an anytime synthesized motion planning algorithm for humanoid robots unifying locomotion and manipulation planning. It generates an entire set of motions to finish specific tasks in an environment containing obstacles by exploiting a powerful inverse kinematics (IK) engine. The IK engine can compute solutions allowing the robot to reposition its feet for meeting the task requirements. The presented planning algorithm has two primary beneficial capabilities. First, it is capable of generating a motion plan to complete a task handling multiple ordered or unordered actions. Second, it produces an initial solution very quickly, and then searches for the opportunity to improve the the solution during execution. The performance of the proposed algorithm is evaluated on the NASA-JSC Valkyrie humanoid robot by demonstrating an object pick up task in simulation and a box pick-and-place task in the real world.

Motions of a robot interacting with its environment can be described by a set of constraints. This paper introduces an approach, called motion template, which can quickly program and compose the constraints for the motion planner to generate the trajectory. Two types of motion templates, grasp and turn, are specifically described to explain the details of the technique. The reusability and shareability properties of the motion template are demonstrated using a variety of the motion planning applications across different robot platforms. A motion template framework is used to implement the motion template with the trajectory optimization.

We propose a shared control architecture to enable the modeling of human-in-the-loop cyber physical systems (HiLCPS) in robotics applications. We identify challenges that currently hinder ideas and concepts from cross-domain applications to be shared among different implementation of HiLCPS. The presented architecture is developed with the intent to help bridge the gap between different communities developing HiLCPS by providing a common framework, associated metrics, and associated language to describe individual elements. We provide examples from two different domains, disaster robotics and assistive robotics, to demonstrate the structure of the architecture.

In this work, we are presenting a navigation framework for electric wheelchairs which integrates various alternative input interfaces (voice control with Google Glass, voice control with CMU Sphinx, and facial expression control with Emotiv EPOC) with assistive navigation. Assistive navigation compensates for the limitations of the alternative interfaces by implementing obstacle avoidance. This enables people with limited control over their limbs to freely navigate within indoor environments. A set of use cases designed to ensure safe and reliable navigation is also presented.

Passive averaging suspensions have been proven highly effective on rovers for improving mobility by providing ground compliance. However, the passive degree of freedom poses an added challenge to the controls problem. This paper presents a controller design to increase the accuracy of straight line trajectories for rovers with passive suspension on rough terrain. The chosen approach uses only proprioceptive sensors, a 3D kinematic model, and a trivial ground plane estimator algorithm to adjust individual wheel velocities based on estimates of terrain slope. This has distinct advantages to other techniques that use global position sensors and dynamic models which inevitably lead to more complex and computationally intensive solutions. The proposed controller is simulated in Matlab and found to be successful through experiments conducted with ORYX 2.0, a planetary rover research platform. This paper presents the feed forward velocity controller design, simulations, and experimental results for validation.

This work presents a hierarchical navigation architecture and cascade classifier for sample search and identification on a space exploration rover. A three tier navigation architecture and inverse Jacobian based robot arm controller are presented. The algorithms are implemented on AERO, the Autonomous Exploration Rover, participating in the NASA Sample Return Robot Centennial Challenge in 2013 and initial results are demonstrated.

This paper describes the design and implementation of the controls and power plant for a robotic arm for physical human-robot interaction on a cyber-physical wheelchair system. There are almost 50 million people in the US who have some degree of disability, and more than 6.5 million of them experience problems with self-care. The aim of this research is to develop a system to control a modular cable-driven arm which will allow locked-in individuals, who are unable to interact with the physical world through movement and speech, to perform activities of daily living (ADL). We present the design of a compact power plant for the 5DOF arm. The robot control is implemented using the Robot Operating System (ROS) framework.

This paper presents the design and experimental validation of a modular robotic platform for planetary exploration. A rover, ORYX 2.0, is designed and developed to serve as a research platform that can transport payloads over rough terrain. Field testing is conducted to evaluate the mobility potential of ORYX 2.0's passive kinematic suspension. Data from field testing is analyzed to identify the wheel velocities over rough terrain when straight line trajectories are desired. A simulation tool is used to estimate the terrain profile, using the 3-axis orientation data and angle of the rocking suspension. The approach used in estimating terrain profiles can effectively be used to improve the rover performance for trajectory tracking control.

This paper introduces the manipulability and maneuverability ellipsoids for two underwater vehicles with on-board manipulators cooperating to carry a common rigid object. The forward kinematics problem for the system formed by the two underwater vehicle-manipulator mechanisms and the rigid load is studied in details. The assignment of the coordinate frames and the selection of a set of generalized coordinates to describe the system configuration are discussed. The pseudovelocities are introduced in order to incorporate the kinematic constraint equations into the kinematic velocity relations. A kinematic model is formulated for the system to be used for defining the manipulability and maneuverability ellipsoids.

This paper studies the inverse kinematics problem for two underwater vehicles with on-board manipulators cooperating to carry a common rigid object. The coordinate frames are assigned to the system formed by the two underwater vehicle-manipulator mechanisms and the rigid load and a set of generalized coordinates are selected to describe the system configuration. The kinematic position and velocity relations are obtained. A kinematic model is formulated for the system to be used for solving the inverse kinematics problem. The pseudovelocities are introduced in order to incorporate the kinematic constraint equations into the kinematic velocity relations. A task-priority redundancy resolution technique is presented in which the kinematic redundancy is used to accomplish secondary tasks of the design choice.

The modeling of two cooperating underwater vehicles with on-board manipulators carrying a common rigid load is studied. The velocity relations and the kinematic coupling are discussed. The kinematic constraints are due to fact that the velocities of the manipulator end-effectors are related via the rigid load. For the entire system, a dynamical model that takes into account the hydrodynamic effects is established for the design of a controller. The dynamical model is based on the Lagrange formulation and is developed in the world coordinate frame. The dynamical coupling arises as a result of the forces and moments transmitted through the common load. Several properties of the system under consideration are also discussed.