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

TL;DR

This paper presents HiFAR, a multi-stage curriculum learning framework that enables humanoid robots to autonomously recover from falls in complex, real-world scenarios with high success and robustness.

Contribution

The study introduces a staged curriculum learning approach for high-dynamics fall recovery, improving stability and adaptability in humanoid robots.

Findings

High success rates in real-world fall recovery

Rapid recovery times demonstrated

Robustness and generalization confirmed

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.