We study the problem of rapidly identifying contact dynamics of unknown objects in partially known environments. The key innovation of our method is a novel formulation of the contact dynamics estimation problem as the joint estimation of contact geometries and physical parameters. We leverage DeepSDF, a compact and expressive neural-network-based geometry representation over a distribution of geometries, and adopt a particle filter to estimate both the geometries in contact and the physical parameters. In addition, we couple the estimator with an active exploration strategy that plans information-gathering moves to further expedite online estimation. Through simulation and physical experiments, we show that our method estimates accurate contact dynamics with fewer than 30 exploration moves for unknown objects touching partially known environments.

翻译：本研究探讨了在部分已知环境中快速识别未知物体接触动力学的问题。本方法的核心创新在于将接触动力学估计问题重新表述为接触几何形态与物理参数的联合估计问题。我们利用DeepSDF——一种基于神经网络的紧凑而富有表现力的几何分布表示方法，并采用粒子滤波器同时估计接触几何形态和物理参数。此外，我们将估计器与主动探索策略相结合，该策略通过规划信息采集动作来进一步加速在线估计过程。通过仿真和物理实验表明，对于接触部分已知环境的未知物体，我们的方法仅需少于30次探索动作即可实现精确的接触动力学估计。