Deep Reactive Planning in Dynamic Environments

TL;DR

This paper introduces a novel deep reactive planning method enabling robots to adapt to changing environments and goals during execution by combining traditional planning, deep learning, and reinforcement learning.

Contribution

It presents an end-to-end policy learning approach that handles dynamic goals and environments, bridging the gap between human-like reflexes and robotic planning.

Findings

Successful simulation of reaching and pick-and-place tasks

Real-world validation on a 6-DoF industrial manipulator

Demonstrated adaptability to environment changes

Abstract

The main novelty of the proposed approach is that it allows a robot to learn an end-to-end policy which can adapt to changes in the environment during execution. While goal conditioning of policies has been studied in the RL literature, such approaches are not easily extended to cases where the robot's goal can change during execution. This is something that humans are naturally able to do. However, it is difficult for robots to learn such reflexes (i.e., to naturally respond to dynamic environments), especially when the goal location is not explicitly provided to the robot, and instead needs to be perceived through a vision sensor. In the current work, we present a method that can achieve such behavior by combining traditional kinematic planning, deep learning, and deep reinforcement learning in a synergistic fashion to generalize to arbitrary environments. We demonstrate the proposed approach for several reaching and pick-and-place tasks in simulation, as well as on a real system of a 6-DoF industrial manipulator. A video describing our work could be found \url{https://youtu.be/hE-Ew59GRPQ}.