Efficient Bimanual Handover and Rearrangement via Symmetry-Aware Actor-Critic Learning

Bimanual manipulation is important for building intelligent robots that unlock richer skills than single arms. We consider a multi-object bimanual rearrangement task, where a reinforcement learning (RL) agent aims to jointly control two arms to rearrange these objects as fast as possible. Solving this task efficiently is challenging for an RL agent due to the requirement of discovering precise intra-arm coordination in exponentially large control space. We develop a symmetryaware actor-critic framework that leverages the interchangeable roles of the two manipulators in the bimanual control setting to reduce the policy search space. To handle the compositionality over multiple objects, we augment training data with an objectcentric relabeling technique. The overall approach produces an RL policy that can rearrange up to 8 objects with a success rate of over 70% in simulation. We deploy the policy to two Franka Panda arms and further show a successful demo on human-robot collaboration.