21 citations · 21 across the 7 of their papers we have counts for
7 papers
A roadmap for AI in robotics
Aude Billard, Alin Albu-Schaeffer, Michael Beetz +8
AI technologies, including deep learning, large-language models have gone from one breakthrough to the other. As a result, we are witnessing growing excitement in robotics at the p…
Reciprocal Learning of Intent Inferral with Augmented Visual Feedback for Stroke
Jingxi Xu, Ava Chen, Lauren Winterbottom +5
Intent inferral, the process by which a robotic device predicts a user's intent from biosignals, offers an effective and intuitive way to control wearable robots. Classical intent…
Fabric Sensing of Intrinsic Hand Muscle Activity
Katelyn Lee, Runsheng Wang, Ava Chen +9
Wearable robotics have the capacity to assist stroke survivors in assisting and rehabilitating hand function. Many devices that use surface electromyographic (sEMG) for control rel…
Grasp Force Assistance via Throttle-based Wrist Angle Control on a Robotic Hand Orthosis for C6-C7 Spinal Cord Injury
Joaquin Palacios, Alexandra Deli-Ivanov, Ava Chen +4
Individuals with hand paralysis resulting from C6-C7 spinal cord injuries frequently rely on tenodesis for grasping. However, tenodesis generates limited grasping force and demands…
RR: Rapid eXploration for Reinforcement Learning via Sampling-based Reset Distributions and Imitation Pre-training
Gagan Khandate, Tristan L. Saidi, Siqi Shang +5
We present a method for enabling Reinforcement Learning of motor control policies for complex skills such as dexterous manipulation. We posit that a key difficulty for training suc…
MORPH: Design Co-optimization with Reinforcement Learning via a Differentiable Hardware Model Proxy
Zhanpeng He, Matei Ciocarlie
We introduce MORPH, a method for co-optimization of hardware design parameters and control policies in simulation using reinforcement learning. Like most co-optimization methods, M…