Achieving successful robotic manipulation is an essential step towards robots being widely used in industry and home settings. Recently, many learning-based methods have been proposed to tackle this challenge, with imitation learning showing great promise. However, imperfect demonstrations and a lack of feedback from teleoperation systems may lead to poor or even unsafe results. In this work we explore the effect of demonstrator force feedback on imitation learning, using a feedback glove and a robot arm to render fingertip-level and palm-level forces, respectively. 10 participants recorded 5 demonstrations of a pick-and-place task with 3 grippers, under conditions with no force feedback, fingertip force feedback, and fingertip and palm force feedback. Results show that force feedback significantly reduces demonstrator fingertip and palm forces, leads to a lower variation in demonstrator forces, and recorded trajectories that are quicker to execute. Using behavioral cloning, we find that agents trained to imitate these trajectories mirror these benefits, even though agents have no force data shown to them during training. We conclude that immersive demonstrations, achieved with force feedback, may be the key to unlocking safer, quicker-to-execute dexterous manipulation policies.

The application of mechanical and other physical properties to the development of robotic systems that can easily adapt to changing external situations is known as mechanical intelligence. Following this concept, many robot hand designs can produce self-adaptive and versatile grasps with simple underactuated fingers and open-loop control, while mechanical- intelligent strategies for dexterous manipulation are still limited. This paper proposes a mechanical-intelligent technique to facilitate dexterous manipulation, in particular prehensile inhand manipulation. The proposed strategy is based on the generation of complex spatial trajectories of the hand-object system, controlled in open loop with the minimum number of actuators and using simple low-level non-position modes. This approach is exemplified by the rigorous analysis and testing of a three-fingered two-actuator underactuated robot hand, called the helical hand, which is capable of generating helical prehensile in-hand manipulation of diversiform objects under error tolerance controlled by constant speed algorithm.

When performing cloth-related tasks, such as garment hanging, it is often important to identify and grasp certain structural regions—a shirt's collar as opposed to its sleeve, for instance. However, due to cloth deformability, these manipulation activities, which are essential in domestic, health care, and industrial contexts, remain challenging for robots. In this paper, we focus on how to segment and grasp structural regions of clothes to enable manipulation tasks, using hanging tasks as case study. To this end, a neural network-based perception system is proposed to segment a shirt's collar from areas that represent the rest of the scene in a depth image. With a 10-minute video of a human manipulating shirts to train it, our perception system is capable of generalizing to other shirts regardless of texture as well as to other types of collared garments. A novel grasping strategy is then proposed based on the segmentation to determine grasping pose. Experiments demonstrate that our proposed grasping strategy achieves 92%, 80%, and 50% grasping success rates with one folded garment, one crumpled garment and three crumpled garments, respectively. Our grasping strategy performs considerably better than tested baselines that do not take into account the structural nature of the garments. With the proposed region segmentation and grasping strategy, challenging garment hanging tasks are successfully implemented using an open-loop control policy. Supplementary material is available at https://sites.google.com/view/garment-hanging

Achieving instinctive multi-grasp control of prosthetic hands typically still requires a large number of sensors, such as electromyography (EMG) electrodes mounted on a residual limb, that can be costly and time consuming to position, with their signals difficult to classify. Deep-learning-based EMG classifiers however have shown promising results over traditional methods, yet due to high computational requirements, limited work has been done with in-prosthetic training. By targeting specific muscles non-invasively, separating grasping action into hold and release states, and implementing data augmentation, we show in this paper that accurate results for embedded, instinctive, multi-grasp control can be achieved with only 2 low-cost sensors, a simple neural network, and minimal amount of training data. The presented controller, which is based on only 2 surface EMG (sEMG) channels, is implemented in an enhanced version of the OLYMPIC prosthetic hand. Results demonstrate that the controller is capable of identifying all 7 specified grasps and gestures with 93% accuracy, and is successful in achieving several real-life tasks in a real world setting.

Using a delta-manipulator for stabilisation of an end-effector to perform precise spatial positioning is a current area of interest in aerial manipulation. High speed precision movements of a manipulator can cause disturbances to the aerial platform, which hinders trajectory tracking and in some cases could be sufficient to cause a loss of control of the vehicle. In this paper, a statically balanced delta aerial manipulator is developed and evaluated. The system is balanced using three counter-masses to reduce the force imparted onto the base and thus reduce perturbations to the movement of the drone. The system is thoroughly tested following trajectories while mounted to a force sensor and while on-board an aerial vehicle. Results show that the forces transmitted to the base in all axes are reduced considerably, however improvements in overall flight accuracy are not observed in aerial settings. Design lessons to make a balanced delta-manipulator viable for practical implementation on an aerial vehicle are discussed in depth. A video summarising the flight testing results is available at https://youtu.be/fXKnosnVKCk.

Through the implementation of reconfigurability to achieve flexibility and adaptation to tasks by morphology changes rather than by increasing the number of joints,malleable robotspresent advantages over traditional serial robot arms in regards to reduced weight, size, and cost. While limited in degrees of freedom (DOF), malleable robots still provide versatility across operations typically served by systems using higher DOF than required by the tasks. In this article, we present the creation of a 2-DOF malleable robot, detailing the design of joints and malleable link, along with its modeling through forward and inverse kinematics, and a reconfiguration methodology that informs morphology changes based on end effector location—determining how the user should reshape the robot to enable a task previously unattainable. The recalibration and motion planning for making robot motion possible after reconfiguration are also discussed, and thorough experiments with the prototype to evaluate accuracy and reliability of the system are presented. Results validate the approach and pave the way for further research in the area.

Mechanical intelligence is the use of mechanical and other physical properties to create robotic systems adaptable to new external situations using simple control schemes. Designs of robot hands have successfully been developed and optimised following this principle to produce self-adaptive and versatile power grasps via implementations based on underactuated fingers, elastic components, and open-loop motor control. However, these characteristics, and mechanical-intelligent strategies in general, have been seldom leveraged for precision grasping. This paper proposes a mechanical-intelligent technique to facilitate not only spiral caging power grasp, but also self-adaptive precision grasp with error tolerance. This approach is exemplified by the rigorous analysis, development, and testing of a novel three-fingered, two-actuator, underactuated robot hand, called the helical hand, which is capable of self-adaptive precision grasping, and of generating spiral helical power grasps of unknown objects by simply setting two actuators at a constant speed.

We introduce an underactuated differential-based robot gripper able to perform self-adaptive grasping with passive disturbance rejection. The gripper utilises three car differential systems to achieve self-adaptiveness with a single actuator: a base differential for distributing power from the motor to the fingers, and two independent finger differentials for controlling the proximal and distal joints. Linear and torsional springs are cleverly added to these differentials to allow the return of the fingers and the gripper-object system to equilibrium, thus enabling the gripper rejecting unexpected external forces applied to the fingers after securing a grasp. This novel design provides passive disturbance rejection without implementing complicated control systems and is the main contribution of this paper. Moreover, the differentials allow the gripper to perform not only self-adaptive power grasp but also precision grasp, provide it with a large force transmission efficiency, and facilitate the prediction of grasping position. We analyse the static model of the introduced differential system and evaluate the gripper design via four sets of experiments. Numerical and empirical results clearly demonstrate the viability of the proposed grasper.

For the majority of tasks performed by traditional serial robot arms, such as bin picking or pick and place, only two or three degrees of freedom (DOF) are required for motion; however, by augmenting the number of degrees of freedom, further dexterity of robot arms for multiple tasks can be achieved. Instead of increasing the number of joints of a robot to improve flexibility and adaptation, which increases control complexity, weight, and cost of the overall system, malleable robots utilise a variable stiffness link between joints allowing the relative positioning of the revolute pairs at each end of the link to vary, thus enabling a low DOF serial robot to adapt across tasks by varying its workspace. In this paper, we present the design and prototyping of a 2-DOF malleable robot, calculate the general equation of its workspace using a parameterisation based on distance geometry-suitable for robot arms of variable topology, and characterise the workspace categories that the end effector of the robot can trace via reconfiguration. Through the design and construction of the malleable robot we explore design considerations, and demonstrate the viability of the overall concept. By using motion tracking on the physical robot, we show examples of the infinite number of workspaces that the introduced 2-DOF malleable robot can achieve.

The Jacobian matrix is a highly popular tool for the control and performance analysis of closed-loop robots. Its usefulness in parallel mechanisms is certainly apparent, and its application to solve motion planning problems, or other higher level questions, has been seldom queried, or limited to nonredundant systems. In this article, we discuss the shortcomings of the use of the Jacobian matrix under redundancy, in particular when applied to kinematically redundant parallel architectures with non-serially connected actuators. These architectures have become fairly popular recently as they allow the end-effector to achieve full rotations, which is an impossible task with traditional topologies.The problems with the Jacobian matrix in these novel systems arise from the need to eliminate redundant variables forming it, resulting in both situations where the Jacobian incorrectly identifies singularities (false positive), and where it fails to identify singularities (false negative). These issues have, thus far, remained unaddressed in the literature. We highlight these limitations herein by demonstrating several cases using numerical examples of both planar and spatial architectures.

Robotic arms built from stiffness-adjustable, continuously bending segments serially connected with revolute joints have the ability to change their mechanical architecture and workspace, thus allowing high flexibility and adaptation to different tasks with less than six degrees of freedom, a concept that we call malleable robots. Known stiffening mechanisms may be used to implement suitable links for these novel robotic manipulators; however, these solutions usually show a reduced performance when bending due to structural deformation. By including an inner support structure this deformation can be minimised, resulting in an increased stiffening performance. This paper presents a new multi-material spine-inspired flexible structure for providing support in stiffness-controllable layer-jamming-based robotic links of large diameter. The proposed spine mechanism is highly movable with type and range of motions that match those of a robotic link using solely layer jamming, whilst maintaining a hollow and light structure. The mechanics and design of the flexible spine are explored, and a prototype of a link utilising it is developed and compared with limb segments based on granular jamming and layer jamming without support structure. Results of experiments verify the advantages of the proposed design, demonstrating that it maintains a constant central diameter across bending angles and presents an improvement of more than 203% of resisting force at 180°.

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.

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.

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.

This paper presents a first attempt at a unified kinematics analysis of all serial and parallel solvable robots, that is, robots whose position analysis can be carried out without relying on numerical methods. The efforts herein are focused on finding a unified formulation for all quartically-solvable robots, as all other solvable robots can be seen as particular cases of them. The first part is centered on the quest for the most general quartically-solvable parallel and serial robots. As a result, representatives of both classes are selected. Then, using Distance Geometry, it is shown how solving the forward kinematics of the parallel representative is equivalent to solve the inverse kinematics of the serial representative, thus providing a unified formulation. Finally, it is shown that the position and singularity analysis of these robots reduces to the analysis of the relative position of two coplanar ellipses.

Research focus on service robots that deals with applications related to healthcare, logistics, residential, search and rescue are gaining significant momentum in the recent years. Their social and economic relevance is more than evident. Yet, while much has been researched about “designing robots” focusing on sensing, actuation, mobility and control of service robots, little work has been done on “design for robots” that looks at designing preferred artefacts or environments for such robots. In this work, we propose a new philosophy of robot inclusive spaces, a cross disciplinary approach that brings together roboticians, architects and designers to solve numerous unsettled research problems in robotics community through design of inclusive interior spaces for robots where the latter live and operate. With a residential floor cleaning robot as a case study, we inductively derived a set of four design principles namely observability, accessibility, activity and safety that guides the realization of an inclusive space for these service robots. Also, the suggested principles are further defined, analysed and validated for their merits in this paper.

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.

A double banana is defined as the bar-and-joint assembly of two bipyramids joined by their apexes. Clearly, the bar lengths of this kind of assembly are not independent as we cannot assign arbitrary values to them. This dependency can be algebraically expressed as a closure condition fully expressed in terms of bar lengths. This paper is devoted to its derivation and to show how its use simplifies the position analysis of many well-known serial and parallel robots thus providing a unifying treatment to apparently disparate problems. This approach permits deriving the univariate polynomials, needed for the closed-form solution of these position analysis problems, without relying on trigonometric substitutions or difficult variable eliminations.

Legged robots are able to move across irregular terrains and some can be energy efficient, but are often constrained by a limited range of gaits which can limit their locomotion capabilities considerably. This paper reports a reconfigurable design approach to robotic legged locomotion that produces a wide variety of gait cycles, opening new possibilities for innovative applications. In this paper, we present a distance-based formulation and its application to solve the position analysis problem of a standard Theo Jansen mechanism. By changing the configuration of a linkage, our objective in this study is to identify novel gait patterns of interest for a walking platform. The exemplary gait variations presented in this work demonstrate the feasibility of our approach, and considerably extend the capabilities of the original design to not only produce novel cum useful gait patterns but also to realize behaviors beyond locomotion.

The real roots of the univariate polynomial closure condition of a planar parallel robot determine the solutions of its forward kinematics. This paper shows how the univariate polynomials of all fully parallel planar robots can be derived directly from that of the widely known 3-RPR robot by simply formulating these polynomials in terms of distances and oriented areas. This is a relevant result because it avoids the case-by-case treatment that requires different sets of variable eliminations to obtain the univariate polynomial of each fully parallel planar robot.

In most practical implementations of the Gough-Stewart platform, the octahedral form is either taken as it stands or is approximated. The kinematics of this particular instance of the Gough-Stewart platform, commonly known as the octahedral manipulator, has been thoughtfully studied. It is well-known, for example, that its forward kinematics can be solved by computing the roots of an octic polynomial and its singularities have a simple geometric interpretation in terms of the intersection of four planes in a single point. In this paper, using a distance-based formulation, it is shown how these properties can be derived without relying neither on variable eliminations nor trigonometric substitutions. Moreover, thanks to this formulation, a family of platforms kinematically equivalent to the octahedral manipulator is obtained. Herein, two Gough-Stewart parallel platforms are said to be kinematically equivalent if there is a one-to-one correspondence between their squared leg lengths for the same configuration of their moving platforms with respect to their bases. If this condition is satisfied, it can be shown that both platforms have the same assembly modes and their singularities, in the configuration space of the moving platform, are located in the same place.

The standard forward-kinematics analysis of 3-RPR planar parallel robots boils down to computation of the roots of a sextic polynomial. There are many different ways to obtain this polynomial, but most of them include exceptions for which the formulation is not valid. Unfortunately, near these exceptions, the corresponding polynomial exhibits numerical instabilities. In this paper, we provide a way around this inconvenience by translating the forward-kinematics problem to be solved into an equivalent problem fully stated in terms of distances. Using constructive geometric arguments, an alternative sextic - which is not linked to a particular reference frame - is straightforwardly obtained with the need for neither variable eliminations nor tangent-half-angle substitutions. The presented formulation is valid, with no modification, for any planar 3-RPR parallel robot, including the special architectures and configurations - which ultimately lead to numerical instabilities - that cannot be directly handled by previous formulations.