Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments

10/22/2020
by   Jun Yamada, et al.
9

Deep reinforcement learning (RL) agents are able to learn contact-rich manipulation tasks by maximizing a reward signal, but require large amounts of experience, especially in environments with many obstacles that complicate exploration. In contrast, motion planners use explicit models of the agent and environment to plan collision-free paths to faraway goals, but suffer from inaccurate models in tasks that require contacts with the environment. To combine the benefits of both approaches, we propose motion planner augmented RL (MoPA-RL) which augments the action space of an RL agent with the long-horizon planning capabilities of motion planners. Based on the magnitude of the action, our approach smoothly transitions between directly executing the action and invoking a motion planner. We evaluate our approach on various simulated manipulation tasks and compare it to alternative action spaces in terms of learning efficiency and safety. The experiments demonstrate that MoPA-RL increases learning efficiency, leads to a faster exploration, and results in safer policies that avoid collisions with the environment. Videos and code are available at https://clvrai.com/mopa-rl .

READ FULL TEXT

page 2

page 3

research
11/11/2021

Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation

Learning complex manipulation tasks in realistic, obstructed environment...
research
08/18/2020

ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation

Many Reinforcement Learning (RL) approaches use joint control signals (p...
research
07/30/2023

Robust Unmanned Surface Vehicle Navigation with Distributional Reinforcement Learning

Autonomous navigation of Unmanned Surface Vehicles (USV) in marine envir...
research
04/18/2023

Safety Guaranteed Manipulation Based on Reinforcement Learning Planner and Model Predictive Control Actor

Deep reinforcement learning (RL) has been endowed with high expectations...
research
10/07/2021

Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks

Realistic manipulation tasks require a robot to interact with an environ...
research
03/24/2021

CLAMGen: Closed-Loop Arm Motion Generation via Multi-view Vision-Based RL

We propose a vision-based reinforcement learning (RL) approach for close...
research
04/23/2020

Guided Dyna-Q for Mobile Robot Exploration and Navigation

Model-based reinforcement learning (RL) enables an agent to learn world ...

Please sign up or login with your details

Forgot password? Click here to reset