主动激发接触模式，让机器人仿真更贴近真实世界。
Exciting Contact Modes in Differentiable Simulations for Robot Learning
- 用信息论方法优化接触点，主动寻找高信息量的接触模式。
- 参数估计误差降低超84%，显著优于随机采样。
- 适合需要精准物理建模的机器人学习研究者。
本文提出一种在可微分模拟器中主动规划并激发接触模式的方法，以缩小仿真到现实的差距。基于信息论的最优实验设计，结合隐式接触优化，识别并搜索高信息量的接触模式。我们在未知惯性与运动学参数的机器人参数估计任务中验证该方法，主动寻求与附近表面的接触。结果表明，相比随机采样基线，本方法使参数估计误差至少降低84%，信息增益显著更高。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。