Broadcasting Support Relations Recursively from Local Dynamics for Object Retrieval in Clutters

TL;DR

This paper presents a novel recursive broadcasting method to accurately infer support relations among cluttered objects, enabling robots to retrieve target objects more efficiently in complex scenes.

Contribution

The paper introduces a recursive broadcasting approach for support relation inference, reducing complexity and improving accuracy in cluttered object retrieval tasks.

Findings

Method improves support relation inference accuracy

Approach reduces computational complexity

Effective in both simulation and real-world scenarios

Abstract

In our daily life, cluttered objects are everywhere, from scattered stationery and books cluttering the table to bowls and plates filling the kitchen sink. Retrieving a target object from clutters is an essential while challenging skill for robots, for the difficulty of safely manipulating an object without disturbing others, which requires the robot to plan a manipulation sequence and first move away a few other objects supported by the target object step by step. However, due to the diversity of object configurations (e.g., categories, geometries, locations and poses) and their combinations in clutters, it is difficult for a robot to accurately infer the support relations between objects faraway with various objects in between. In this paper, we study retrieving objects in complicated clutters via a novel method of recursively broadcasting the accurate local dynamics to build a support relation graph of the whole scene, which largely reduces the complexity of the support relation inference and improves the accuracy. Experiments in both simulation and the real world demonstrate the efficiency and effectiveness of our method.