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PDF 635 KB
DOI: http://dx.doi.org/10.7551/978-0-262-32621-6-ch024
Pages 146-147
First published 30 July 2014

Learning to Walk in Every Direction with the TBR-Learning algorithm

Antoine Cully and Jean-Baptiste Mouret

Abstract (Excerpt)

Legged robots are versatile machines that can outperform wheeled robots on rough terrain (Raibert, 1986), for instance in exploration or rescue missions. Their versatility is, however, tempered by their mechanical and control complexity, which makes them prone to mechanical damages and difficult to control robustly (Raibert, 1986; Bongard et al., 2006; Koos et al., 2013a). A promising way to compensate for these two weaknesses is to let robots discover on their own the best way to move in the current situation. A legged robot can thus cope with an unexpected terrain or with mechanical damages by learning a new walking gait (Bongard et al., 2006; Koos et al., 2013a), in the same way as animals can learn to limp with a sprained ankle.