Haptic Sorter: A Unified Planning Framework for Online Shape Estimation and Real-Time Pose Inference

2026-05-29

AI Summary

This work addresses the challenge in robotic manipulation where object shape and pose are often unknown due to sensor noise and occlusion, rendering conventional planning methods ineffective. The authors propose a unified framework that integrates tactile perception, geometric modeling, and motion planning: active tactile exploration is guided by Bayesian optimization to approximate object boundaries online using superellipses; an adaptive manipulation potential field encodes geometric constraints; and object pose is inferred in real time through an ordinary differential equation (ODE)-based estimator. Evaluated on 2D sorting tasks, the approach demonstrates strong robustness and generalization across diverse object geometries, achieving efficient real-time shape estimation and pose inference in both simulation and physical multi-arm robotic platforms.

Abstract

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.

Problem

• robotic manipulation
• shape estimation
• pose inference
• haptic perception
• sensor uncertainty

Innovation

• Bayesian Optimization
• Haptic Perception
• Shape Estimation
• Real-Time Pose Inference
• Geometric Modeling