HALO:Closing Sim-to-Real Gap for Heavy-loaded Humanoid Agile Motion Skills via Differentiable Simulation

• URL: http://arxiv.org/abs/2603.15084v1
• Date: Mon, 16 Mar 2026 10:38:05 GMT
• Title: HALO:Closing Sim-to-Real Gap for Heavy-loaded Humanoid Agile Motion Skills via Differentiable Simulation
• Abstract summary: We propose a two-stage gradient-based system identification framework built on the differentiable simulator MuJoCo XLA.<n>The first stage calibrates the nominal robot model using real-world data to reduce intrinsic sim-to-real discrepancies.<n>The second stage further identifies the mass distribution of the unknown payload.
• Score: 22.6954724792851
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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. Project Page: https://mwondering.github.io/halo-humanoid/

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