Abstract: Emulating human-like dexterity with robotic hands has been a long-standing challenge in robotics. In recent years, machine learning has demanded robot hands to be reliable, inexpensive and easy-to-reproduce. For the past few years we have been investigating how to address these demands. We will demonstrate our three robot hands that address this problem ranging from rigid easy-to-simulate hand to soft but strong dexterous robot hands performing three different machine learning tasks. Our first machine learning task will be teleoperation, where we will develop a new mobile arm and hand motion capture system that we will bring to RSS 2024. Second, we will demonstrate how to use human-video and human motion to teach robot hands. Finally, we will show how to continually improve these policies using reinforcement learning in both simulation and the real-world. This demo will be engaging, will serve to demystify dexterous manipulation and inspire researchers to bring robot hands into their own projects. Please see our website at https://leaphand.com/rss2024demo for more interactive information.
