Dynamic Handover: Throw and Catch with Bimanual Hands
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/
Cite
Text
Huang et al. "Dynamic Handover: Throw and Catch with Bimanual Hands." Conference on Robot Learning, 2023.Markdown
[Huang et al. "Dynamic Handover: Throw and Catch with Bimanual Hands." Conference on Robot Learning, 2023.](https://mlanthology.org/corl/2023/huang2023corl-dynamic/)BibTeX
@inproceedings{huang2023corl-dynamic,
title = {{Dynamic Handover: Throw and Catch with Bimanual Hands}},
author = {Huang, Binghao and Chen, Yuanpei and Wang, Tianyu and Qin, Yuzhe and Yang, Yaodong and Atanasov, Nikolay and Wang, Xiaolong},
booktitle = {Conference on Robot Learning},
year = {2023},
pages = {1887-1902},
volume = {229},
url = {https://mlanthology.org/corl/2023/huang2023corl-dynamic/}
}