Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human Player

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Abstract

This paper presents a sensor-level mapless collision avoidance algorithm for use in mobile robots that map raw sensor data to linear and angular velocities and navigate in an unknown environment without a map. An efficient training strategy is proposed to allow a robot to learn from both human experience data and self-exploratory data. A game format simulation framework is designed to allow the human player to tele-operate the mobile robot to a goal and human action is also scored using the reward function. Both human player data and self-playing data are sampled using prioritized experience replay algorithm. The proposed algorithm and training strategy have been evaluated in two different experimental configurations: Environment 1, a simulated cluttered environment, and Environment 2, a simulated corridor environment, to investigate the performance. It was demonstrated that the proposed method achieved the same level of reward using only 16% of the training steps required by the standard Deep Deterministic Policy Gradient (DDPG) method in Environment 1 and 20% of that in Environment 2. In the evaluation of 20 random missions, the proposed method achieved no collision in less than 2 h and 2.5 h of training time in the two Gazebo environments respectively. The method also generated smoother trajectories than DDPG. The proposed method has also been implemented on a real robot in the real-world environment for performance evaluation. We can confirm that the trained model with the simulation software can be directly applied into the real-world scenario without further fine-tuning, further demonstrating its higher robustness than DDPG. The video and code are available: https://youtu.be/BmwxevgsdGc https://github.com/hanlinniu/turtlebot3_ddpg_collision_avoidance

Original languageEnglish
Title of host publication2021 IEEE/SICE International Symposium on System Integration, SII 2021
PublisherIEEE
Pages144-149
Number of pages6
ISBN (Electronic)9781728176581
ISBN (Print)9781728176581
DOIs
Publication statusPublished - 24 Mar 2021
Event2021 IEEE/SICE International Symposium on System Integration, SII 2021 - Virtual, Iwaki, Fukushima, Japan
Duration: 11 Jan 202114 Jan 2021

Publication series

Name2021 IEEE/SICE International Symposium on System Integration, SII 2021

Conference

Conference2021 IEEE/SICE International Symposium on System Integration, SII 2021
Country/TerritoryJapan
CityVirtual, Iwaki, Fukushima
Period11/01/2114/01/21

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