Keywords: Bimanual Dexterous Manipulation, Sim-to-Real Transfer
TL;DR: We propose a throwing and catching system using bimanual dexterous hands with reinforcement learning.
Abstract: Humans throw and catch objects all the time. However, such a seemingly common skill introduces a lot of challenges for robots to achieve: The robots need to operate such dynamic actions at high-speed, collaborate precisely, and interact with diverse objects. In this paper, we design a system with two multi-finger hands attached to robot arms to solve this problem. We train our system using Multi-Agent Reinforcement Learning in simulation and perform Sim2Real transfer to deploy on the real robots. To overcome the Sim2Real gap, we provide multiple novel algorithm designs including learning a trajectory prediction model for the object. Such a model can help the robot catcher has a real-time estimation of where the object will be heading, and then react accordingly. We conduct our experiments with multiple objects in the real-world system, and show significant improvements over multiple baselines. Our project page is available at https://binghao-huang.github.io/dynamic_handover/
Student First Author: yes
Supplementary Material: zip
Instructions: I have read the instructions for authors (https://corl2023.org/instructions-for-authors/)
Publication Agreement: pdf
Poster Spotlight Video: mp4