Learning to Singulate Objects in Packed Environments using a Dexterous Hand

TL;DR

This paper introduces SOPE, a reinforcement learning framework enabling a dexterous robot hand to effectively singulate objects in cluttered, packed environments, with successful transfer from simulation to real-world scenarios.

Contribution

The paper presents a novel displacement-based state representation and multi-phase RL approach for object singulation with a 16-DOF hand in tight spaces, demonstrating real-world applicability.

Findings

Achieved 79.2% success rate in physical trials.

Outperformed alternative methods in cluttered environments.

Successfully transferred policies from simulation to real robot.

Abstract

Robotic object singulation, where a robot must isolate, grasp, and retrieve a target object in a cluttered environment, is a fundamental challenge in robotic manipulation. This task is difficult due to occlusions and how other objects act as obstacles for manipulation. A robot must also reason about the effect of object-object interactions as it tries to singulate the target. Prior work has explored object singulation in scenarios where there is enough free space to perform relatively long pushes to separate objects, in contrast to when space is tight and objects have little separation from each other. In this paper, we propose the Singulating Objects in Packed Environments (SOPE) framework. We propose a novel method that involves a displacement-based state representation and a multi-phase reinforcement learning procedure that enables singulation using the 16-DOF Allegro Hand. We demonstrate extensive experiments in Isaac Gym simulation, showing the ability of our system to singulate a target object in clutter. We directly transfer the policy trained in simulation to the real world. Over 250 physical robot manipulation trials, our method obtains success rates of 79.2%, outperforming alternative learning and non-learning methods.