用神经几何表示与粒子滤波，快速估计未知物体的接触动力学。
Tactile Probabilistic Contact Dynamics Estimation of Unknown Objects
- 结合DeepSDF与粒子滤波，联合估计接触几何与物理参数。
- 在30次以内探索动作下实现高精度接触动力学估计。
- 适合机器人触觉感知与未知物体交互场景使用。
我们研究在部分已知环境中快速识别未知物体接触动力学的问题。方法的核心创新在于将接触动力学估计问题重新建模为接触几何与物理参数的联合估计。利用DeepSDF——一种基于神经网络的紧凑且表达能力强的几何分布表示，结合粒子滤波器，同时估计接触中的几何形状和物理参数。此外，该估计算法耦合了主动探索策略，可规划信息采集动作以加速在线估计过程。通过仿真与实物实验验证，本方法在未知物体接触部分已知环境时，仅需少于30次探索动作即可实现准确的动力学估计。
原文摘要 · Abstract (English)
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.
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