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@InProceedings(icra04,
        author="Nate Kohl and Peter Stone",
        title="Policy Gradient Reinforcement Learning for Fast Quadrupedal Locomotion",
        year="2004",month="May",
        booktitle="Proceedings of the {IEEE} International Conference on Robotics and Automation",
        abstract={
                 This paper presents a machine learning approach to
                 optimizing a quadrupedal trot gait for forward speed.
                 Given a parameterized walk designed for a specific
                 robot, we propose using a form of policy gradient
                 reinforcement learning to automatically search the
                 set of possible parameters with the goal of finding
                 the fastest possible walk.  We implement and test our
                 approach on a commercially available quadrupedal
                 robot platform, namely the Sony Aibo robot.  After
                 about three hours of learning, all on the physical
                 robots and with no human intervention other than to
                 change the batteries, the robots achieved the fastest
                 walk known for the Aibo, significantly outperforming
                 a variety of existing hand-coded and learned
                 solutions.
        },
        wwwnote={Some <a href="http://www.cs.utexas.edu/users/AustinVilla/legged/learned-walk/">videos of walking robots</a> referenced in the paper.<br>
                 <a href="http://www.egr.msu.edu/ralab/icra2004/">ICRA 2004</a>},
)