Dynamic Handover: Throw and Catch with Bimanual Hands
This addresses the challenge of dynamic bimanual manipulation for robots, which is incremental as it builds on existing reinforcement learning and Sim2Real methods.
The paper tackles the problem of enabling robots to perform high-speed, precise dynamic handovers (throw and catch) with diverse objects, achieving significant improvements over baselines in real-world experiments.
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 \url{https://binghao-huang.github.io/dynamic_handover/}.