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The Chin Pinch: A Case Study in Skill Learning on a Legged Robot

Peggy Fidelman and Peter Stone. The Chin Pinch: A Case Study in Skill Learning on a Legged Robot. In Gerhard Lakemeyer, Elizabeth Sklar, Domenico Sorenti, and Tomoichi Takahashi, editors, RoboCup-2006: Robot Soccer World Cup X, Lecture Notes in Artificial Intelligence, pp. 59–71, Springer Verlag, Berlin, 2007.
Some videos referenced in the paper.

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Abstract

When developing skills on a physical robot, it is appealing to turn to modern machine learning methods in order to automate the process. However, when no accurate simulator exists for the type of motion in question, all learning must occur on the physical robot itself. In such a case, there is a high premium on quick, efficient learning (specifically, learning with low sample complexity). Recent results in learning locomotion have demonstrated the feasibility of learning fast walks directly on quadrupedal robots. This paper demonstrates that it is also possible to learn a higher-level skill requiring more fine motor coordination, again with all learning occurring directly on the robot. In particular, the paper presents a learned ball-grasping skill on a commercially available Sony Aibo robot, with no human intervention other than battery changes. The learned skill significantly outperforms our best hand-tuned solution. As the learned grasping skill relies on a learned walk, we characterize our learning implementation within the layered learning formalism. To our knowledge, the two learned layers represent the first use of layered learning on a physical robot.

BibTeX

@incollection(LNAI2006-peggy,
        author="Peggy Fidelman and Peter Stone",
        title="The Chin Pinch:  A Case Study in Skill Learning on a Legged Robot",
        booktitle= "{R}obo{C}up-2006: Robot Soccer World Cup {X}",
        Editor="Gerhard Lakemeyer and Elizabeth Sklar and Domenico Sorenti and Tomoichi Takahashi",
        Publisher="Springer Verlag",address="Berlin",year="2007",
        issn="0302-9743",
        isbn="978-3-540-74023-0",
        series="Lecture Notes in Artificial Intelligence",      
	volume="4434",
        pages="59--71",
        abstract={
                  When developing skills on a physical robot, it is
                  appealing to turn to modern machine learning methods
                  in order to automate the process.  However, when no
                  accurate simulator exists for the type of motion in
                  question, all learning must occur on the physical
                  robot itself.  In such a case, there is a high
                  premium on quick, efficient learning (specifically,
                  learning with low sample complexity).  Recent
                  results in learning locomotion have demonstrated the
                  feasibility of learning fast walks directly on
                  quadrupedal robots.  This paper demonstrates that it
                  is also possible to learn a higher-level skill
                  requiring more fine motor coordination, again with
                  all learning occurring directly on the robot.  In
                  particular, the paper presents a learned
                  ball-grasping skill on a commercially available Sony
                  Aibo robot, with no human intervention other than
                  battery changes.  The learned skill significantly
                  outperforms our best hand-tuned solution.  As the
                  learned grasping skill relies on a learned walk, we
                  characterize our learning implementation within the
                  layered learning formalism.  To our knowledge, the
                  two learned layers represent the first use of
                  layered learning on a physical robot.
        },
        wwwnote={Some <a href="http://www.cs.utexas.edu/~AustinVilla/legged/learned-acquisition/">videos</a> referenced in the paper.},
)

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