Robotics manipulation usually assumes that the shape and pose of the object are known to the robot prior to motion planning. However, precise geometric information is not always available in practice, and pose inference suffers from sensor uncertainties and view occlusion. In this work, we propose a unified model-based geometric framework integrating robotic haptic perception, modeling, and manipulation planning. Our novelties involve: \textit{i)} Introducing Bayesian Optimization (BO) to guide the haptic exploration for object shape inference, where superellipses are used to approximate geometric boundary; \textit{ii)} Adaptive formulation of manipulation potential encoding object geometry for quasi-static robot-object interaction; \textit{iii)} Proposing an online Ordinary Differential Equation (ODE) for real-time pose inference based on model prediction and tactile feedback. We deploy our system on a 2D robotic sorting task, and vary object geometries to validate the robustness and generalizability of our framework in both simulation and a real-world multi-arm setup.

翻译：机器人操作通常假设物体形状和位姿在运动规划之前已知。然而，实际场景中难以获取精确几何信息，且位姿推断易受传感器不确定性与视图遮挡影响。本文提出一种统一的、基于模型的几何框架，融合机器人触觉感知、建模与操作规划。创新点包括：\textit{i)}引入贝叶斯优化引导触觉探索以推断物体形状，采用超椭圆近似几何边界；\textit{ii)}提出自适应操作势能公式，编码物体几何特征以表征准静态机器人-物体交互；\textit{iii)}提出在线常微分方程方法，基于模型预测与触觉反馈实现实时位姿推断。我们在二维机器人分拣任务中部署该系统，通过改变物体几何形状在仿真与真实多臂平台中验证框架的鲁棒性与泛化能力。