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.

翻译：本文探索了一种在可微分仿真器中主动规划与激励接触模式的方法，旨在缩小仿真与现实的差距。我们提出了一种源自信息论方法的最优实验设计方法，通过使用接触隐式优化来识别并搜索信息丰富的接触模式。我们在一个机器人参数估计问题上验证了所提方法，该问题涉及未知的惯性和运动学参数，并主动寻求与附近表面的接触。实验结果表明，与随机采样基线相比，我们的方法在实验运行中将未知参数估计的识别误差降低了至少$\sim 84\%$，并获得了显著更高的信息增益。