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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2507.23445 |
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| _version_ | 1866915419697184768 |
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| author | Kawachi, Yuta |
| author_facet | Kawachi, Yuta |
| contents | Simulation-to-real transfer using domain randomization for robot control often relies on low-gear-ratio, backdrivable actuators, but these approaches break down when the sim-to-real gap widens. Inspired by the traditional PID controller, we reinterpret its gains as surrogates for complex, unmodeled plant dynamics. We then introduce a physics-guided gain regularization scheme that measures a robot's effective proportional gains via simple real-world experiments. Then, we penalize any deviation of a neural controller's local input-output sensitivities from these values during training. To avoid the overly conservative bias of naive domain randomization, we also condition the controller on the current plant parameters. On an off-the-shelf two-wheeled balancing robot with a 110:1 gearbox, our gain-regularized, parameter-conditioned RNN achieves angular settling times in hardware that closely match simulation. At the same time, a purely domain-randomized policy exhibits persistent oscillations and a substantial sim-to-real gap. These results demonstrate a lightweight, reproducible framework for closing sim-to-real gaps on affordable robotic hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23445 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantifying and Visualizing Sim-to-Real Gaps: Physics-Guided Regularization for Reproducibility Kawachi, Yuta Robotics Systems and Control Simulation-to-real transfer using domain randomization for robot control often relies on low-gear-ratio, backdrivable actuators, but these approaches break down when the sim-to-real gap widens. Inspired by the traditional PID controller, we reinterpret its gains as surrogates for complex, unmodeled plant dynamics. We then introduce a physics-guided gain regularization scheme that measures a robot's effective proportional gains via simple real-world experiments. Then, we penalize any deviation of a neural controller's local input-output sensitivities from these values during training. To avoid the overly conservative bias of naive domain randomization, we also condition the controller on the current plant parameters. On an off-the-shelf two-wheeled balancing robot with a 110:1 gearbox, our gain-regularized, parameter-conditioned RNN achieves angular settling times in hardware that closely match simulation. At the same time, a purely domain-randomized policy exhibits persistent oscillations and a substantial sim-to-real gap. These results demonstrate a lightweight, reproducible framework for closing sim-to-real gaps on affordable robotic hardware. |
| title | Quantifying and Visualizing Sim-to-Real Gaps: Physics-Guided Regularization for Reproducibility |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2507.23445 |