Stabilize to Act: Learning to Coordinate for Bimanual Manipulation

Bimanual coordination is pervasive, spanning household activities such as cutting food, surgical skills such as suturing a wound, or industrial tasks such as connecting two cables. In robotics, the addition of a second arm opens the door to a higher level of task complexity, but comes with a number of control challenges. With a second arm, we have to reason about how to produce coordinated behavior in a higher dimensional action space, resulting in more computationally challenging learning, planning, and optimization problems. The addition of a second arm also complicates data collection--it requires teleoperating a robot with more degrees of freedom--which hinders our ability to rely on methods that require expert bimanual demonstrations. To combat these challenges, we can draw inspiration from how humans tackle bimanual tasks--specifically alternating between using one arm to stabilize parts of the environment, then using the other arm to act conditioned on the stabilized state of the world. Alternating stabilizing and acting offers a significant gain over both model-based and data-driven prior approaches for bimanual manipulation. Previous model-based techniques have proposed planning algorithms for bimanual tasks such as collaborative transport or scooping [1, 2, 3], but require hand-designed specialized primitives or follow predefined trajectories limiting their abilities to learn new skills or adapt. On another extreme, we turn to reinforcement learning (RL) techniques that do not need costly primitives. However, RL methods are notoriously data hungry and a high-dimensional bimanual action space further exacerbates this problem.