6 accepted papers
Motion planning is a critical aspect of lower-limb exoskeleton control. Conventional motion planning approaches compute reference trajectories in task space, leading to infeasible or uncomfortable joint level motions. Conversely, joint space planning methods ensure feasibility but may fail to reprod
One of the most important research challenges in upper-limb prosthetics is enhancing the user-prosthesis communication to closely resemble the experience of a natural limb. As prosthetic devices become more complex, users often struggle to control the additional degrees of freedom. In this context,
<p>iHannes dataset used in the paper "Continuous Wrist Control on the Hannes Prosthesis: A Vision-Based Shared Autonomy Framework", accepted to ICRA2025</p>
Recent advancements in control of prosthetic hands have focused on increasing autonomy through the use of cameras and other sensory inputs. These systems aim to reduce the cognitive load on the user by automatically controlling certain degrees of freedom. In robotics, imitation learning has emerged
Noninvasive human-machine interfaces such as surface electromyography (sEMG) have long been employed for controlling robotic prostheses. However, classical controllers are limited to few degrees of freedom (DoF). More recently, machine learning methods have been proposed to learn personalized contro
The quality of life for upper limb amputees can be greatly improved by the adoption of poly-articulated myoelectric prostheses. Typically, in these applications, a pattern recognition algorithm is used to control the system by converting the recorded electromyographic activity (EMG) into complex mul