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