6 accepted papers
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 lac…
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 ca…
Human hands are able to grasp a wide range of object sizes, shapes, and weights, achieved via reshaping and altering their apparent grasping stiffness between compliant power and rigid precision. Achieving similar versatility in robotic hands remains a challenge, which has often been addressed by ad
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…
Virtual reality (VR) rehabilitation systems have been proposed to enable prosthetic hand users to perform training before receiving their prosthesis. Improving pre-prosthetic training to be more representative and better prepare the patient for prosthesis use is a crucial step forwards in rehabilita
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…