# Motion Strategy Generation Based on Multimodal Motion Primitives and Reinforcement Learning Imitation for Quadruped Robots

## TL;DR

This paper introduces a new method for quadruped robots to generate complex motions using motion primitives and reinforcement learning, reducing the need for manual design.

## Contribution

A novel motion policy generation method combining multimodal motion primitives and imitation learning for quadruped robots.

## Key findings

- A multimodal motion library was successfully constructed using 3D engine design and motion capture data.
- The proposed method enabled precise trajectory tracking and rapid policy deployment on the Lite3 quadruped robot.
- Simulation and physical experiments validated the effectiveness of the approach for complex motion strategies.

## Abstract

With the advancement of task-oriented reinforcement learning (RL), the capability of quadruped robots for motion generation and complex task completion has significantly improved. However, current control strategies require extensive domain expertise and time-consuming design processes to acquire operational skills and achieve multi-task motion control, often failing to effectively manage complex behaviors composed of multiple coordinated actions. To address these limitations, this paper proposes a motion policy generation method for quadruped robots based on multimodal motion primitives and imitation learning. A multimodal motion library was constructed using 3D engine motion design, motion capture data retargeting, and trajectory planning. A temporal domain-based behavior planner was designed to combine these primitives and generate complex behaviors. We developed a RL-based imitation learning training framework to achieve precise trajectory tracking and rapid policy deployment, ensuring the effective application of actions/behaviors on the quadruped platform. Simulation and physical experiments conducted on the Lite3 quadruped robot validated the efficacy of the proposed approach, offering a new paradigm for the deployment and development of motion strategies for quadruped robots.