RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards

Fatemeh Zargarbashi, Jin Cheng, Dongho Kang, Robert Sumner, Stelian Coros

Keywords: Legged robots, Multi-Critic Reinforcement Learning, Motion Imitation

TL;DR: This paper presents a novel learning-based control framework that incorporates high-level objectives for the natural locomotion of legged robots through keyframing.

Abstract: This paper presents a novel learning-based control framework that uses keyframing to incorporate high-level objectives in natural locomotion for legged robots. These high-level objectives are specified as a variable number of partial or complete pose targets that are spaced arbitrarily in time. Our proposed framework utilizes a multi-critic reinforcement learning algorithm to effectively handle the mixture of dense and sparse rewards. Additionally, it employs a transformer-based encoder to accommodate a variable number of input targets, each associated with specific time-to-arrivals. Throughout simulation and hardware experiments, we demonstrate that our framework can effectively satisfy the target keyframe sequence at the required times. In the experiments, the multi-critic method significantly reduces the effort of hyperparameter tuning compared to the standard single-critic alternative. Moreover, the proposed transformer-based architecture enables robots to anticipate future goals, which results in quantitative improvements in their ability to reach their targets.

Website: https://sites.google.com/view/robot-keyframing

Student Paper: yes

Submission Number: 183