Nonprehensile Planar Manipulation through Reinforcement Learning with Multimodal Categorical Exploration

TL;DR

This paper introduces a multimodal categorical exploration method in reinforcement learning to improve dexterous nonprehensile planar manipulation, enabling more accurate, robust, and transferable pushing policies for robotic manipulation tasks.

Contribution

It proposes a novel multimodal exploration strategy using categorical distributions to better capture hybrid dynamics in RL for robotic pushing tasks.

Findings

Enhanced accuracy in pushing policies

Robustness to disturbances and noise

Successful transfer from simulation to physical robot

Abstract

Developing robot controllers capable of achieving dexterous nonprehensile manipulation, such as pushing an object on a table, is challenging. The underactuated and hybrid-dynamics nature of the problem, further complicated by the uncertainty resulting from the frictional interactions, requires sophisticated control behaviors. Reinforcement Learning (RL) is a powerful framework for developing such robot controllers. However, previous RL literature addressing the nonprehensile pushing task achieves low accuracy, non-smooth trajectories, and only simple motions, i.e. without rotation of the manipulated object. We conjecture that previously used unimodal exploration strategies fail to capture the inherent hybrid-dynamics of the task, arising from the different possible contact interaction modes between the robot and the object, such as sticking, sliding, and separation. In this work, we propose a multimodal exploration approach through categorical distributions, which enables us to train planar pushing RL policies for arbitrary starting and target object poses, i.e. positions and orientations, and with improved accuracy. We show that the learned policies are robust to external disturbances and observation noise, and scale to tasks with multiple pushers. Furthermore, we validate the transferability of the learned policies, trained entirely in simulation, to a physical robot hardware using the KUKA iiwa robot arm. See our supplemental video: https://youtu.be/vTdva1mgrk4.