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PolySim: Full-Body Humanoid Control Using Multiple Simulators
Outline
This paper presents a training platform called PolySim, which trains policies using multiple heterogeneous simulators to address the sim-to-real gap problem that arises from inherent assumptions and limitations of simulators in simulation-based humanoid whole body control (WBC) policies. PolySim implements dynamic-level domain randomization by simultaneously running parallel environments on multiple simulators. Theoretically, we demonstrate that PolySim provides a tighter upper bound on simulator-induced bias than single-simulator training. Experimental results demonstrate that PolySim significantly reduces motion tracking errors in sim-to-sim evaluations (e.g., a 52.8% success rate improvement over IsaacSim-based approaches on MuJoCo) and enables zero-shot deployment on a real Unitree G1 robot without additional fine-tuning, demonstrating effective transfer from simulation to real environments.
Takeaways, Limitations
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Takeaways:
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Bridging the sim-to-real gap with multi-simulator learning.
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Improved performance compared to single simulator training (sim-to-sim, real robot deployment).
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Theoretical analysis of simulator-induced bias.
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Limitations:
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Information about the specific types of simulators or hardware used in this paper may be lacking.
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Further research is needed on the efficiency and generalizability of PolySim.
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There is a need to verify adaptability to various variability in real environments.
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PolySim code will be made public after the paper is published.