ResMimic: From General Motion Tracking to Humanoid Whole-Body Loco-Manipulation via Residual Learning

Siheng Zhao1,2§, Yanjie Ze1,3§, Yue Wang2, C. Karen Liu1,3†, Pieter Abbeel1,4†, Guanya Shi1,5†, Rocky Duan1†,
1Amazon FAR (Frontier AI & Robotics)   2University of Southern California   3Stanford University   4UC Berkeley   5Carnegie Mellon University
§Work done while interning at Amazon FAR   †FAR Co-Lead
arXiv Paper Video tl;dr Code (Coming Soon)

We present ResMimic, a two-stage residual framework that unleashes the power of pre-trained general motion tracking policy. It enables expressive whole-body loco-manipulation with payloads up to 5.5kg without task-specific design, generalizes across poses, and exhibits reactive behavior.

Abstract

Humanoid whole-body loco-manipulation promises transformative capabilities for daily service and warehouse tasks. While recent advances in general motion tracking (GMT) have enabled humanoids to reproduce diverse human motions, these policies lack the precision and object awareness required for loco-manipulation. To this end, we introduce ResMimic, a two-stage residual learning framework for precise and expressive humanoid control from human motion data. First, a GMT policy, trained on large-scale human-only motion, serves as a task-agnostic base for generating human-like whole-body movements. An efficient but precise residual policy is then learned to refine the GMT outputs to improve locomotion and incorporate object interaction. To further facilitate efficient training, we design (i) a point-cloud-based object tracking reward for smoother optimization, (ii) a contact reward that encourages accurate humanoid body-object interactions, and (iii) a curriculum-based virtual object controller to stabilize early training. We evaluate ResMimic in both simulation and on a real Unitree G1 humanoid. Results show substantial gains in task success, training efficiency, and robustness over strong baselines.

Method Overview

Method Overview

Expressive Whole-Body Loco-Manipulation

Carry Box onto Back (1x)
Kneel on One Knee & Lift Box (1x)

Heavy Object with Whole-Body Contact

Squat & Lift Box with Whole-Body Contact (1x)
4.5kg

Lift Chair with Whole-Body Contact (1x)

4.5kg

Lift Chair with Whole-Body Contact (1x)

5.5kg

Robustness Test

Lift Chair with Whole-Body Contact (1x)

4.5kg

Lift Chair with Whole-Body Contact (1x)

4.5kg

General Object Interaction

Sit on Chair (1x)

ResMimic (Success ✅)

Sit on Chair (1x)

Base Policy (Failure ❌)

Sit on Chair (1x)


Continuous Execution with MoCap Input

Lift Box with Random Object Initial Pose

Autonomous Consecutive Lift Box


Reactivate Behavior to External Perturbation


Comparison with Baselines

ResMimic (Success ✅)

Base Policy (Failure ❌)

Train from Scratch (Failure ❌)

Base Policy + Finetune (Failure ❌)

BibTeX

@misc{zhao2025resmimicgeneralmotiontracking,
        title={ResMimic: From General Motion Tracking to Humanoid Whole-body Loco-Manipulation via Residual Learning}, 
        author={Siheng Zhao and Yanjie Ze and Yue Wang and C. Karen Liu and Pieter Abbeel and Guanya Shi and Rocky Duan},
        year={2025},
        eprint={2510.05070},
        archivePrefix={arXiv},
        primaryClass={cs.RO},
        url={https://arxiv.org/abs/2510.05070}, 
  }