Discovering Self-Protective Falling Policy for Humanoid Robot via Deep Reinforcement Learning

2025-12-01

AI Summary

Humanoid robots are prone to hardware damage and environmental hazards during falls due to their high center of mass, large mass, and high degrees of freedom; existing control methods relying on hand-crafted priors suffer from poor generalization and adaptability. This paper proposes a data-driven protective falling strategy learning framework: leveraging deep reinforcement learning with domain-adaptive curriculum training, it autonomously discovers fall mitigation strategies—primarily adopting “triangular-structure” postures to dissipate impact energy—in simulation. A novel dynamic reward function and multi-scenario domain-diverse curriculum eliminate reliance on strong human priors. Built upon high-dimensional state-space modeling, the method enables efficient sim-to-real transfer. Experiments demonstrate substantial reductions in peak impact force and injury risk across diverse falling scenarios; the learned policy is successfully deployed on a real humanoid robot platform, validating its safety, generalizability, and transferability.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Humanoid robots have received significant research interests and advancements in recent years. Despite many successes, due to their morphology, dynamics and limitation of control policy, humanoid robots are prone to fall as compared to other embodiments like quadruped or wheeled robots. And its large weight, tall Center of Mass, high Degree-of-Freedom would cause serious hardware damages when falling uncontrolled, to both itself and surrounding objects. Existing researches in this field mostly focus on using control based methods that struggle to cater diverse falling scenarios and may introduce unsuitable human prior. On the other hand, large-scale Deep Reinforcement Learning and Curriculum Learning could be employed to incentivize humanoid agent discovering falling protection policy that fits its own nature and property. In this work, with carefully designed reward functions and domain diversification curriculum, we successfully train humanoid agent to explore falling protection behaviors and discover that by forming a `triangle' structure, the falling damages could be significantly reduced with its rigid-material body. With comprehensive metrics and experiments, we quantify its performance with comparison to other methods, visualize its falling behaviors and successfully transfer it to real world platform.

Problem

• Develops a falling protection policy for humanoid robots using deep reinforcement learning
• Addresses hardware damage risks from uncontrolled falls in diverse scenarios
• Trains robots to autonomously discover self-protective behaviors like triangle structures

Innovation

• Deep Reinforcement Learning for falling policy
• Curriculum Learning with domain diversification
• Triangle structure reduces falling damage