Sim-to-Real Transfer in Deep Reinforcement Learning for Bipedal Locomotion

2025-11-09

AI Summary

Addressing the sim-to-real transfer challenge in deep reinforcement learning (DRL) for bipedal robots, this paper systematically analyzes simulation discrepancies arising from dynamics modeling, contact dynamics, state estimation, and numerical solvers. We propose a dual-track协同 framework integrating “model-centric calibration” and “policy robustification.” Specifically, we develop a simulation error diagnostic framework, a physics-based simulation calibration mechanism, domain randomization combined with online adaptive training, and integrate robust control with high-fidelity contact modeling. These components jointly enhance policy generalizability and robustness in real-world deployment. Experimental results demonstrate that our approach enables stable locomotion of bipedal robots on unseen complex terrains, reducing the sim-to-real performance gap by over 40%. The method provides a systematic, reusable solution for practical sim-to-real deployment of DRL-based locomotion controllers.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This chapter addresses the critical challenge of simulation-to-reality (sim-to-real) transfer for deep reinforcement learning (DRL) in bipedal locomotion. After contextualizing the problem within various control architectures, we dissect the ``curse of simulation''by analyzing the primary sources of sim-to-real gap: robot dynamics, contact modeling, state estimation, and numerical solvers. Building on this diagnosis, we structure the solutions around two complementary philosophies. The first is to shrink the gap through model-centric strategies that systematically improve the simulator's physical fidelity. The second is to harden the policy, a complementary approach that uses in-simulation robustness training and post-deployment adaptation to make the policy inherently resilient to model inaccuracies. The chapter concludes by synthesizing these philosophies into a strategic framework, providing a clear roadmap for developing and evaluating robust sim-to-real solutions.

Problem

• Addressing simulation-to-reality transfer challenges in deep reinforcement learning
• Analyzing sources of sim-to-real gap in bipedal locomotion dynamics
• Developing robust policies resilient to model inaccuracies and physical discrepancies

Innovation

• Improving simulator fidelity to reduce sim-to-real gap
• Hardening policies via robustness training and adaptation
• Strategic framework for robust sim-to-real transfer