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.

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.

Most state-of-the-art bipedal robots are designed to be anthropomorphic and therefore possess legs with knees. Whilst this facilitates more human-like locomotion, there are implementation issues that make walking with straight or near-straight legs difficult. Most bipedal robots have to move with a constant bend in the legs to avoid singularities at the knee joints, and to keep the centre of mass at a constant height for control purposes. Furthermore, having a knee on the leg increases the design complexity as well as the weight of the leg, hindering the robot's performance in agile behaviours such as running and jumping. We present SLIDER, an ultra-lightweight, low-cost bipedal walking robot with a novel knee-less leg design. This non-anthropomorphic straight-legged design reduces the weight of the legs significantly whilst keeping the same functionality as anthropomorphic legs. Simulation results show that SLIDER's low-inertia legs contribute to less vertical motion in the center of mass (CoM) than anthropomorphic robots during walking, indicating that SLIDER's model is closer to the widely used Inverted Pendulum (IP) model. Finally, stable walking on flat terrain is demonstrated both in simulation and in the physical world, and feedback control is implemented to address challenges with the physical robot.