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X-Loco: Towards Generalist Humanoid Locomotion Control via Synergetic Policy Distillation

Dewei Wang, Xinmiao Wang, Chenyun Zhang, Jiyuan Shi, Yingnan Zhao, Chenjia Bai, Xuelong Li

TL;DR

X-Loco is the first framework to demonstrate vision-based humanoid locomotion that jointly integrates upright locomotion, whole-body coordination and fall recovery, while operating solely under velocity commands without relying on reference motions.

Abstract

While recent advances have demonstrated strong performance in individual humanoid skills such as upright locomotion, fall recovery and whole-body coordination, learning a single policy that masters all these skills remains challenging due to the diverse dynamics and conflicting control objectives involved. To address this, we introduce X-Loco, a framework for training a vision-based generalist humanoid locomotion policy. X-Loco trains multiple oracle specialist policies and adopts a synergetic policy distillation with a case-adaptive specialist selection mechanism, which dynamically leverages multiple specialist policies to guide a vision-based student policy. This design enables the student to acquire a broad spectrum of locomotion skills, ranging from fall recovery to terrain traversal and whole-body coordination skills. To the best of our knowledge, X-Loco is the first framework to demonstrate vision-based humanoid locomotion that jointly integrates upright locomotion, whole-body coordination and fall recovery, while operating solely under velocity commands without relying on reference motions. Experimental results show that X-Loco achieves superior performance, demonstrated by tasks such as fall recovery and terrain traversal. Ablation studies further highlight that our framework effectively leverages specialist expertise and enhances learning efficiency.

X-Loco: Towards Generalist Humanoid Locomotion Control via Synergetic Policy Distillation

TL;DR

X-Loco is the first framework to demonstrate vision-based humanoid locomotion that jointly integrates upright locomotion, whole-body coordination and fall recovery, while operating solely under velocity commands without relying on reference motions.

Abstract

While recent advances have demonstrated strong performance in individual humanoid skills such as upright locomotion, fall recovery and whole-body coordination, learning a single policy that masters all these skills remains challenging due to the diverse dynamics and conflicting control objectives involved. To address this, we introduce X-Loco, a framework for training a vision-based generalist humanoid locomotion policy. X-Loco trains multiple oracle specialist policies and adopts a synergetic policy distillation with a case-adaptive specialist selection mechanism, which dynamically leverages multiple specialist policies to guide a vision-based student policy. This design enables the student to acquire a broad spectrum of locomotion skills, ranging from fall recovery to terrain traversal and whole-body coordination skills. To the best of our knowledge, X-Loco is the first framework to demonstrate vision-based humanoid locomotion that jointly integrates upright locomotion, whole-body coordination and fall recovery, while operating solely under velocity commands without relying on reference motions. Experimental results show that X-Loco achieves superior performance, demonstrated by tasks such as fall recovery and terrain traversal. Ablation studies further highlight that our framework effectively leverages specialist expertise and enhances learning efficiency.
Paper Structure (58 sections, 11 equations, 6 figures, 11 tables, 1 algorithm)

This paper contains 58 sections, 11 equations, 6 figures, 11 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Learning-based Humanoid Control
  4. Upright Locomotion
  5. Fall Recovery
  6. Motion Tracking
  7. Multiple Locomotion Skills Learning
  8. Problem Formulation
  9. State Space
  10. Action Space
  11. Method
  12. Specialist Policies Training
  13. Upright Locomotion Specialist
  14. Fall Recovery Specialist
  15. Whole-Body Coordination Specialist
  16. ...and 43 more sections

Figures (6)

  • Figure 1: Overview of X-Loco. (a) X-Loco integrates the capabilities of three specialist policies into a vision-based generalist policy via Synergetic Policy Distillation. (b) X-Loco can perform diverse locomotion skills in the real world.
  • Figure 2: Terrains used for training and evaluation.
  • Figure 3: Top: Testing the generalist policy on hybrid, challenging terrains. Bottom: Extensibility of the X-Loco to include vision-guided (a) lateral sidling and (b) kneeling crawling.
  • Figure 4: Distillation loss curves of ablation on SAR.
  • Figure 5: Left: (a) fall recovery and platform traversal; (b) resilience to external disturbances; (c) a continuous sequence of recovery, stair climbing, and rolling under an overhead bar. Right: (Top) a failure case where the robot trips due to lack of camera randomization. (Bottom, from left to right) original depth in simulation, noisy depth, and processed real-world depth.
  • ...and 1 more figures