HALO:Closing Sim-to-Real Gap for Heavy-loaded Humanoid Agile Motion Skills via Differentiable Simulation
Abstract
Humanoid robots deployed in real-world scenarios often need to carry unknown payloads, which introduce significant mismatch and degrade the effectiveness of simulation-to-reality reinforcement learning methods. To address this challenge, we propose a two-stage gradient-based system identification framework built on the differentiable simulator MuJoCo XLA. The first stage calibrates the nominal robot model using real-world data to reduce intrinsic sim-to-real discrepancies, while the second stage further identifies the mass distribution of the unknown payload. By explicitly reducing structured model bias prior to policy training, our approach enables zero-shot transfer of reinforcement learning policies to hardware under heavy-load conditions. Extensive simulation and real-world experiments demonstrate more precise parameter identification, improved motion tracking accuracy, and substantially enhanced agility and robustness compared to existing baselines.
Method
Overview of HALO:(a)Data Collection:Trajectories are collected under both loaded and unloaded conditions using exploration policy trained with wide DR, followed by real-world deployment with a fixed foot constraint.(b)Data Processing:Full-body trajectories reconstruction from joint-state measurements via forward kinematics and foot-height alignment.(c)Two-stage Payload-related Parameter Identification:Stage 1 optimize the full set of model parameters to yield a calibrated base model using trajectories without payload. Based on the calibrated model, stage 2 optimize only the payload-related parameters, using trajectories collected under loaded conditions. (d)Heavy-loaded Motion Skills:The accurate identified model parameters enabling zero-shot sim-to-real transfer of the learned skills to the physical heavy-loaded humanoid.
BibTeX
@misc{wang2026haloclosingsimtorealgapheavyloaded,
title={HALO:Closing Sim-to-Real Gap for Heavy-loaded Humanoid Agile Motion Skills via Differentiable Simulation},
author={Xingyi Wang and Chenyun Zhang and Weiji Xie and Chao Yu and Wei Song and Chenjia Bai and Shiqiang Zhu},
year={2026},
eprint={2603.15084},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2603.15084},
}