Robots operating in an open world will encounter novel objects with unknown physical properties, such as mass, friction, or size. These robots will need to sense these properties through interaction prior to performing downstream tasks with the objects. We propose a method that autonomously learns tactile exploration policies by developing a generative world model that is leveraged to 1) estimate the object's physical parameters using a differentiable Bayesian filtering algorithm and 2) develop an exploration policy using an information-gathering model predictive controller. We evaluate our method on three simulated tasks where the goal is to estimate a desired object property (mass, height or toppling height) through physical interaction. We find that our method is able to discover policies that efficiently gather information about the desired property in an intuitive manner. Finally, we validate our method on a real robot system for the height estimation task, where our method is able to successfully learn and execute an information-gathering policy from scratch.

翻译：在开放世界中运行的机器人会遇到具有未知物理属性（如质量、摩擦力或尺寸）的新物体。这些机器人需要在执行下游任务之前通过交互感知这些属性。我们提出一种自主学习触觉探索策略的方法，该方法通过开发生成式世界模型来实现：1）利用可微贝叶斯滤波算法估计物体的物理参数，2）利用信息收集模型预测控制器制定探索策略。我们在三个模拟任务上评估了该方法，这些任务的目标是通过物理交互估计期望的物体属性（质量、高度或倾倒高度）。我们发现，该方法能够以直观的方式发现可高效收集目标属性信息的策略。最后，我们在真实机器人系统上针对高度估计任务验证了该方法，我们的方法能够从零开始成功学习并执行信息收集策略。