HiFAR: Multi-Stage Curriculum Learning for High-Dynamics Humanoid Fall Recovery (2502.20061v2)

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.

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

The research on humanoid robots' ability to recover autonomously from falls, especially in complex and unstructured environments, is a significant challenge. Traditional control methods fall short in addressing the high-dimensional dynamics and contact-intensive scenarios associated with fall recovery. Reinforcement Learning (RL) approaches, burdened by sparse rewards and gaps between simulations and real-world applications, also face problems. This paper proposes a novel multi-stage curriculum learning framework named HiFAR, aimed at improving humanoid fall recovery through staged learning approaches.

Overview of HiFAR Framework

The HiFAR framework employs a multi-stage curriculum learning paradigm. This systematic approach incrementally introduces high-dimensional recovery tasks with increasing complexity, enabling the acquisition of efficient and stable recovery strategies in humanoid robots. This method is tested on a real humanoid robot, showcasing high success rates, rapid recovery times, and robustness across a broad range of fall scenarios.

The framework breaks down the complex task of fall recovery into manageable stages, each characterized by a distinct level of task dimensionality and complexity. The initial stage operates in a two-dimensional setting to establish fundamental recovery strategies. The second stage transitions to a more complex three-dimensional task environment, accommodating additional constraints and variability.

Key Contributions

The paper introduces several key innovations:

1. Stage Division Strategy: The fall recovery task is divided into manageable low-dimensional tasks, gradually increasing complexity to simplify the learning process.
2. Reference State Initialization (KSI) and Reward Shaping: These techniques guide the learning process, accelerating convergence and enhancing policy generalization across different scenarios.
3. Dimensionality Expansion: Supplementary actuated joints improve policy robustness, enabling successful deployment across a variety of fall scenarios.

Experimental validation on the Booster T1 humanoid robot demonstrates the approach's adaptability and robustness. The successful sim-to-real transfer reflects in the robot's ability to rapidly stabilize across varying conditions.

Implications and Future Directions

This research has several implications for robotics and control systems:

1. Practical Deployment: Successful fall recovery enhances robot safety and operational effectiveness in dynamic environments, such as household and industrial settings.
2. Theoretical Insights: The effective use of multi-stage learning strategies can guide development in adjacent areas requiring complex motor skill acquisition, such as adaptive locomotion.
3. Future Developments: The potential applications of HiFAR methods could extend to adaptive robotics, evolving policies further to accommodate even more complex scenarios. Enhancements in sensorimotor systems could permit handling of more diversified environmental challenges.

Overall, HiFAR presents practical applications and academic contributions to the field of robotics, emphasizing the efficacy of gradual learning frameworks in complex task environments. Such advancements in humanoid fall recovery are pivotal for improved interaction quality and operational durability in human-robot environments.