Continuous reinforcement learning based ramp jump control for single-track two-wheeled robots

Qingyuan Zheng, Duo Wang, Zhang Chen, Yiyong Sun, Bin Liang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Single-track two-wheeled robots have become an important research topic in recent years, owing to their simple structure, energy savings and ability to run on narrow roads. However, the ramp jump remains a challenging task. In this study, we propose to realize a single-track two-wheeled robot ramp jump. We present a control method that employs continuous action reinforcement learning techniques for single-track two-wheeled robot control. We design a novel reward function for reinforcement learning, optimize the dimensions of the action space, and enable training under the deep deterministic policy gradient algorithm. Finally, we validate the control method through simulation experiments and successfully realize the single-track two-wheeled robot ramp jump task. Simulation results validate that the control method is effective and has several advantages over high-dimension action space control, reinforcement learning control of sparse reward function and discrete action reinforcement learning control.

Original languageEnglish
Pages (from-to)892-904
Number of pages13
JournalTransactions of the Institute of Measurement and Control
Volume44
Issue number4
DOIs
Publication statusPublished - Feb 2022
Externally publishedYes

Keywords

  • Single-track two-wheeled robots
  • continuous reinforcement learning
  • motion control
  • ramp jump control

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