Home Publications Reinforcement learning approach to generate goal-directed locomotion of a snake-like robot with screw-drive units
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S. Chatterjee, T. Nachstedt, Florentin Wörgötter, M. Tamosiunaite, Poramate Manoonpong, Y. Enomoto, R. Arizumi, and F. Matsuno (2014)

Reinforcement learning approach to generate goal-directed locomotion of a snake-like robot with screw-drive units

Konferenzband: 2014 23rd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD):1-7.  (export entry)


In this paper we apply a policy improvement algorithm called Policy Improvement with Path Integrals (PI2) to generate goal-directed locomotion of a complex snake-like robot with screw-drive units. PI2 is numerically simple and has an ability to deal with high dimensional systems. Here, this approach is used to find proper locomotion control parameters, like joint angles and screw-drive velocities, of the robot. The learning process was achieved using a simulated robot and the learned parameters were successfully transferred to the real one. As a result the robot can locomote toward a given goal.
Konferenz: 2014 23rd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD) Bibliothekskatalog: IEEE Xplore DOI: 10.1109/RAAD.2014.7002234