Real-time control of walking
An analytical approach for gait study and its applications on wave gaits
International Journal of Robotics Research
Walknet—a biologically inspired network to control six-legged walking
Neural Networks - Special issue on neural control and robotics: biology and technology
Journal of Intelligent and Robotic Systems
Analysis of wave gaits for energy efficiency
Autonomous Robots
The Study on Optimal Gait for Five-Legged Robot with Reinforcement Learning
ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
Gait generation for a quadruped robot using Kalman filter as optimizer
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Robotics and Autonomous Systems
Switching max-plus models for legged locomotion
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Kinematic and dynamic analysis of a hexapod walking-running-bounding gaits robot and control actions
Computers and Electrical Engineering
Design with shape grammars and reinforcement learning
Advanced Engineering Informatics
Reinforcement learning in robotics: A survey
International Journal of Robotics Research
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
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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.