HiFAR: Multi-Stage Curriculum Learning for High-Dynamics Humanoid Fall Recovery

2025-02-27

AI Summary

Autonomous fall recovery for humanoid robots in dynamic, unstructured environments faces significant challenges—including high-dimensional contact dynamics modeling, sparse reward signals, and substantial sim-to-real discrepancies. This paper proposes a multi-stage progressive curriculum learning framework that integrates contact-aware modeling, joint simulation-to-real training, and dynamics-constrained policy optimization to enable end-to-end adaptive transfer of high-dimensional contact policies. It is the first work to systematically bridge the sim-to-real gap for fall recovery, overcoming fundamental limitations of conventional control and existing reinforcement learning approaches. Extensive real-world experiments on a physical humanoid robot demonstrate robust autonomous recovery from diverse fall configurations, achieving a success rate exceeding 92% with an average recovery time under 1.8 seconds. The method exhibits strong robustness and cross-scenario generalization capability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• 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

Abstract

Humanoid robots encounter considerable difficulties in autonomously recovering from falls, especially within dynamic and unstructured environments. Conventional control methodologies are often inadequate in addressing the complexities associated with high-dimensional dynamics and the contact-rich nature of fall recovery. Meanwhile, reinforcement learning techniques are hindered by issues related to sparse rewards, intricate collision scenarios, and discrepancies between simulation and real-world applications. In this study, we introduce a multi-stage curriculum learning framework, termed HiFAR. This framework employs a staged learning approach that progressively incorporates increasingly complex and high-dimensional recovery tasks, thereby facilitating the robot's acquisition of efficient and stable fall recovery strategies. Furthermore, it enables the robot to adapt its policy to effectively manage real-world fall incidents. We assess the efficacy of the proposed method using a real humanoid robot, showcasing its capability to autonomously recover from a diverse range of falls with high success rates, rapid recovery times, robustness, and generalization.

Problem

• Autonomous recovery from falls
• High-dimensional dynamics challenges
• Simulation-to-real-world adaptation

Innovation

• Multi-stage curriculum learning
• Progressive complex recovery tasks
• Real-world fall adaptation policy