Learning Visual Quadrupedal Loco-Manipulation from Demonstrations
Zhengmao He Kun Lei Yanjie Ze Koushil Sreenath Zhongyu Li Huazhe Xu
Quadruped robots are progressively being integrated into human environments.
Despite the growing locomotion capabilities of quadrupedal robots, their
interaction with objects in realistic scenes is still limited. While additional
robotic arms on quadrupedal robots enable manipulating objects, they are
sometimes redundant given that a quadruped robot is essentially a mobile unit
equipped with four limbs, each possessing 3 degrees of freedom (DoFs). Hence,
we aim to empower a quadruped robot to execute real-world manipulation tasks
using only its legs. We decompose the loco-manipulation process into a
low-level reinforcement learning (RL)-based controller and a high-level
Behavior Cloning (BC)-based planner. By parameterizing the manipulation
trajectory, we synchronize the efforts of the upper and lower layers, thereby
leveraging the advantages of both RL and BC. Our approach is validated through
simulations and real-world experiments, demonstrating the robot's ability to
perform tasks that demand mobility and high precision, such as lifting a basket
from the ground while moving, closing a dishwasher, pressing a button, and
pushing a door. Project website: https://zhengmaohe.github.io/leg-manip