The paper presents a hierarchical learning framework that combines reinforcement learning (RL) and behavior cloning (BC) to enable quadrupedal robots to perform a variety of loco-manipulation tasks. The framework decomposes the loco-manipulation process into a low-level RL-based controller and a high-level BC-based planner.
The low-level controller is trained using RL to enable the robot to track 6-DoF trajectories with its end-effector while maintaining stable locomotion with the other three legs. The high-level planner is trained using BC to efficiently learn manipulation skills from demonstrations, which are collected through parallel simulations.
The authors parameterize the manipulation trajectory of the end-effector to facilitate the integration of RL and BC. This approach also enables easy data collection, eliminating the need for teleoperation and the challenges of aligning human actions with legged robots.
The framework is evaluated on a set of diverse loco-manipulation tasks, including pressing buttons, pulling handles, pushing doors, lifting baskets, opening and closing dishwashers, pulling objects, twisting valves, and shooting balls. The results demonstrate that the proposed method significantly outperforms baseline approaches in terms of success rates across all tasks.
The authors also validate the learned loco-manipulation skills on a real-world Unitree Aliengo quadrupedal robot, showcasing the framework's sim-to-real transfer capabilities.
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