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Real-world autonomous missions often require rich interaction with nearbyobjects, such as doors or switches, along with effective navigation. However,such complex behaviors are difficult to learn because they involve bothhigh-level planning and low-level motor control. We present a novel framework,Cascaded Compositional Residual Learning (CCRL), which learns composite skillsby recursively leveraging a library of previously learned control policies. Ourframework learns multiplicative policy composition, task-specific residualactions, and synthetic goal information simultaneously while freezing theprerequisite policies. We further explicitly control the style of the motion byregularizing residual actions. We show that our framework learns joint-levelcontrol policies for a diverse set of motor skills ranging from basiclocomotion to complex interactive navigation, including navigating aroundobstacles, pushing objects, crawling under a table, pushing a door open withits leg, and holding it open while walking through it. The proposed CCRLframework leads to policies with consistent styles and lower joint torques,which we successfully transfer to a real Unitree A1 robot without anyadditional fine-tuning.

Cascaded Compositional Residual Learning for Complex Interactive Behaviors