Object Rearrangement with Nested Nonprehensile Manipulation Actions

TL;DR

This paper introduces a novel rearrangement planning method using nested nonprehensile actions, enabling robots to efficiently displace multiple objects simultaneously in cluttered environments, demonstrated through simulation and real-world experiments.

Contribution

It proposes a new planning approach that exploits nested pushing actions to move multiple objects at once, reducing trajectory length and complexity.

Findings

Successfully solves complex rearrangement tasks in simulation.

Reduces end-effector trajectory length compared to existing methods.

Effective in real-world robotic experiments with a Kuka arm.

Abstract

This paper considers the problem of rearrangement planning, i.e finding a sequence of manipulation actions that displace multiple objects from an initial configuration to a given goal configuration. Rearrangement is a critical skill for robots so that they can effectively operate in confined spaces that contain clutter. Examples of tasks that require rearrangement include packing objects inside a bin, wherein objects need to lay according to a predefined pattern. In tight bins, collision-free grasps are often unavailable. Nonprehensile actions, such as pushing and sliding, are preferred because they can be performed using minimalistic end-effectors that can easily be inserted in the bin. Rearrangement with nonprehensile actions is a challenging problem as it requires reasoning about object interactions in a combinatorially large configuration space of multiple objects. This work revisits several existing rearrangement planning techniques and introduces a new one that exploits nested nonprehensile actions by pushing several similar objects simultaneously along the same path, which removes the need to rearrange each object individually. Experiments in simulation and using a real Kuka robotic arm show the ability of the proposed approach to solve difficult rearrangement tasks while reducing the length of the end-effector's trajectories.