Towards Adaptive Humanoid Control via Multi-Behavior Distillation and Reinforced Fine-Tuning

TL;DR

This paper introduces a two-stage adaptive humanoid control framework that combines multi-behavior distillation and reinforced fine-tuning to enable robots to perform diverse locomotion skills across various terrains.

Contribution

The proposed method is the first to integrate multi-behavior distillation with reinforced fine-tuning for adaptive humanoid control across multiple skills and terrains.

Findings

The method achieves strong adaptability in simulation and real-world tests.

It outperforms existing approaches in handling irregular terrains.

The approach enables seamless skill switching in diverse environments.

Abstract

Humanoid robots are promising to learn a diverse set of human-like locomotion behaviors, including standing up, walking, running, and jumping. However, existing methods predominantly require training independent policies for each skill, yielding behavior-specific controllers that exhibit limited generalization and brittle performance when deployed on irregular terrains and in diverse situations. To address this challenge, we propose Adaptive Humanoid Control (AHC) that adopts a two-stage framework to learn an adaptive humanoid locomotion controller across different skills and terrains. Specifically, we first train several primary locomotion policies and perform a multi-behavior distillation process to obtain a basic multi-behavior controller, facilitating adaptive behavior switching based on the environment. Then, we perform reinforced fine-tuning by collecting online feedback in performing adaptive behaviors on more diverse terrains, enhancing terrain adaptability for the controller. We conduct experiments in both simulation and real-world experiments in Unitree G1 robots. The results show that our method exhibits strong adaptability across various situations and terrains. Project website: https://ahc-humanoid.github.io.