BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion

2025-08-11

AI Summary

This work addresses the challenge of enabling humanoid robots to efficiently learn and generalize motor skills from human demonstrations. We propose the first diffusion policy distillation framework designed for real-world hardware deployment. Methodologically, it integrates high-fidelity motion capture, guided diffusion-based policy modeling, and cost-function-driven zero-shot control—marking the first application of conditional diffusion models to physical humanoid control. By leveraging simulation-to-real transfer and full-body motion optimization, the framework successfully deploys highly dynamic behaviors—including jump rotations, cartwheels, and sprinting—on real robots. Moreover, it enables zero-shot adaptation to downstream tasks such as waypoint navigation, teleoperation, and obstacle avoidance. Experimental results demonstrate substantial improvements in motion fidelity, cross-task generalization, and deployment flexibility compared to prior approaches.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Diffusion Models for Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• 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

Abstract

Learning skills from human motions offers a promising path toward generalizable policies for whole-body humanoid control, yet two key cornerstones are missing: (1) a high-quality motion tracking framework that faithfully transforms large-scale kinematic references into robust and extremely dynamic motions on real hardware, and (2) a distillation approach that can effectively learn these motion primitives and compose them to solve downstream tasks. We address these gaps with BeyondMimic, the first real-world framework to learn from human motions for versatile and naturalistic humanoid control via guided diffusion. Our framework provides a motion tracking pipeline capable of challenging skills such as jumping spins, sprinting, and cartwheels with state-of-the-art motion quality. Moving beyond mimicking existing motions and synthesize novel ones, we further introduce a unified diffusion policy that enables zero-shot task-specific control at test time using simple cost functions. Deployed on hardware, BeyondMimic performs diverse tasks at test time, including waypoint navigation, joystick teleoperation, and obstacle avoidance, bridging sim-to-real motion tracking and flexible synthesis of human motion primitives for whole-body control. https://beyondmimic.github.io/.

Problem

• Develop high-quality motion tracking for dynamic humanoid movements
• Distill motion primitives for versatile task-solving in humanoid control
• Bridge sim-to-real gap for flexible human motion synthesis

Innovation

• High-quality motion tracking for dynamic humanoid motions
• Guided diffusion learning for versatile humanoid control
• Zero-shot task control using simple cost functions