Exciting Contact Modes in Differentiable Simulations for Robot Learning
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915036195192832 |
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| author | Sathyanarayan, Hrishikesh Abraham, Ian |
| author_facet | Sathyanarayan, Hrishikesh Abraham, Ian |
| contents | In this paper, we explore an approach to actively plan and excite contact modes in differentiable simulators as a means to tighten the sim-to-real gap. We propose an optimal experimental design approach derived from information-theoretic methods to identify and search for information-rich contact modes through the use of contact-implicit optimization. We demonstrate our approach on a robot parameter estimation problem with unknown inertial and kinematic parameters which actively seeks contacts with a nearby surface. We show that our approach improves the identification of unknown parameter estimates over experimental runs by an estimate error reduction of at least $\sim 84\%$ when compared to a random sampling baseline, with significantly higher information gains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10935 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exciting Contact Modes in Differentiable Simulations for Robot Learning Sathyanarayan, Hrishikesh Abraham, Ian Robotics Information Theory In this paper, we explore an approach to actively plan and excite contact modes in differentiable simulators as a means to tighten the sim-to-real gap. We propose an optimal experimental design approach derived from information-theoretic methods to identify and search for information-rich contact modes through the use of contact-implicit optimization. We demonstrate our approach on a robot parameter estimation problem with unknown inertial and kinematic parameters which actively seeks contacts with a nearby surface. We show that our approach improves the identification of unknown parameter estimates over experimental runs by an estimate error reduction of at least $\sim 84\%$ when compared to a random sampling baseline, with significantly higher information gains. |
| title | Exciting Contact Modes in Differentiable Simulations for Robot Learning |
| topic | Robotics Information Theory |
| url | https://arxiv.org/abs/2411.10935 |