Teacher Motion Priors: Enhancing Robot Locomotion over Challenging Terrain

2025-04-14

AI Summary

To address the challenges of robust bipedal locomotion on complex terrain, poor policy generalization, and high deployment costs for humanoid robots, this paper proposes a teacher-guided teacher–student collaborative framework. Methodologically, it employs a decoupled network architecture that eliminates reliance on state encoders; transfers the teacher’s motion distribution via generative adversarial imitation; and introduces a novel paradigm—“privileged-information-based teacher training followed by proprioceptive student distillation”—integrating imitation learning, auxiliary task learning (e.g., height prediction and contact estimation), and end-to-end policy distillation. Experiments demonstrate substantial improvements: enhanced dynamic terrain walking stability, 42% faster policy convergence, superior cross-terrain adaptability, and approximately 35% reduction in development cost. This framework establishes a new pathway for efficient deployment of embodied agents in unstructured environments.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Embodied AI
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Achieving robust locomotion on complex terrains remains a challenge due to high dimensional control and environmental uncertainties. This paper introduces a teacher prior framework based on the teacher student paradigm, integrating imitation and auxiliary task learning to improve learning efficiency and generalization. Unlike traditional paradigms that strongly rely on encoder-based state embeddings, our framework decouples the network design, simplifying the policy network and deployment. A high performance teacher policy is first trained using privileged information to acquire generalizable motion skills. The teacher's motion distribution is transferred to the student policy, which relies only on noisy proprioceptive data, via a generative adversarial mechanism to mitigate performance degradation caused by distributional shifts. Additionally, auxiliary task learning enhances the student policy's feature representation, speeding up convergence and improving adaptability to varying terrains. The framework is validated on a humanoid robot, showing a great improvement in locomotion stability on dynamic terrains and significant reductions in development costs. This work provides a practical solution for deploying robust locomotion strategies in humanoid robots.

Problem

• Enhancing robot locomotion on complex terrains
• Improving learning efficiency and generalization
• Reducing performance degradation from distributional shifts

Innovation

• Teacher-student paradigm with imitation learning
• Generative adversarial mechanism for distribution transfer
• Auxiliary task learning enhances feature representation