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RobotDrlSim: A real time robot simulation platform for reinforcement learning and human interactive demonstration learning

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Abstract

Deep reinforcement learning (DRL) techniques give robotics research an AI boost in many applications. In order to simultaneously accommodate the complex robotic behaviour simulation and DRL algorithm verification, a new simulation platform, namely the RobotDrlSim, is proposed. First, we design a standardized API interfacing mechanism for coordinating diverse environments on RobotDrlSim platform, where PyBullet simulator is equipped with an API to form a physical engine for robotics simulation. Second, benchmark DRL models are included in the baseline library for evaluation. Third, real-time human-robot interactions can be captured and imported to drive the RobotDrlSim tasks, which provide big data-stream for reinforcement learning. Experimentations show that cutting-edge DRL algorithms developed in python can be seamlessly deployed to the robots, and human interactions can be availed in training the robots. RobotDrlSim is valid for efficiently developing DRL algorithms for artificial intelligence models of robots, and it is especially suitable for the robot educational purposes.
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Dates and versions

hal-03021400 , version 1 (24-11-2020)

Identifiers

  • HAL Id : hal-03021400 , version 1

Cite

Te Sun, Liang Gong, Xvdong Li, Shenghan Xie, Zhaorun Chen, et al.. RobotDrlSim: A real time robot simulation platform for reinforcement learning and human interactive demonstration learning. MSOTA 2020 - 3rd International Conference on Modeling, Simulation and Optimization Technologies and Applications, Nov 2020, Beijing / Virtual, China. ⟨hal-03021400⟩
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