The highly varied and deformable structure of clothing presents a challenging task in the area of robot manipulation. Recent literature has shown an increasing interest in this field, however limited information exists on the influence of end-effector selection, instead focusing on the perception, modelling, and methodology in handling fabrics. Here, we present a benchmark set of household clothing items, along with a framework for defining textile features in relation to how the objects can be grasped and manipulated. Alongside these, we present four example benchmarks for evaluating the performance of a robot end-effector in relation to the grasping and manipulation of common pieces of clothing: Edge drag accuracy, edge grasp resilience, grasp encapsulation, and grasp fold generation. We perform these benchmarks on several common robot end-effectors (Franka Emika (FE) Hand with standard and Fin Ray® style fingers (Flex), Robotiq 2F-140, and the Openhand Model T42) and present and discuss their respective performances. Results show that the Robotiq scored highest across most benchmarks, closely followed by the FE hand. The T42 showed excellent encapsulation of items, while the FE (Flex) was particularly successful picking up flat edges.

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.

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.

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°.