Free gait generation with reinforcement learning for a six-legged robot

Mustafa Suphi Erden*, Kemal Leblebicioglu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

73 Citations (Scopus)


In this paper the problem of free gait generation and adaptability with reinforcement learning are addressed for a six-legged robot. Using the developed free gait generation algorithm the robot maintains to generate stable gaits according to the commanded velocity. The reinforcement learning scheme incorporated into the free gait generation makes the robot choose more stable states and develop a continuous walking pattern with a larger average stability margin. While walking in normal conditions with no external effects causing unstability, the robot is guaranteed to have stable walk, and the reinforcement learning only improves the stability. The adaptability of the learning scheme is tested also for the abnormal case of deficiency in one of the rear-legs. The robot gets a negative reinforcement when it falls, and a positive reinforcement when a stable transition is achieved. In this way the robot learns to achieve a continuous pattern of stable walk with five legs. The developed free gait generation with reinforcement learning is applied in real-time on the actual robot both for normal walking with different speeds and learning of five-legged walking in the abnormal case. (c) 2007 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)199-212
Number of pages14
JournalRobotics and Autonomous Systems
Issue number3
Publication statusPublished - 31 Mar 2008


  • locomotion
  • walking
  • six-legged robot
  • reinforcement learning
  • free gait


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