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Exploring nature-inspired robot agility

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  1. skybrian
    From the blog post: [...] [...] [...]

    From the blog post:

    In “Learning Agile Robotic Locomotion Skills by Imitating Animals”, we present a framework that takes a reference motion clip recorded from an animal (a dog, in this case) and uses RL to train a control policy that enables a robot to imitate the motion in the real world. By providing the system with different reference motions, we are able to train a quadruped robot to perform a diverse set of agile behaviors, ranging from fast walking gaits to dynamic hops and turns. The policies are trained primarily in simulation, and then transferred to the real world using a latent space adaptation technique that can efficiently adapt a policy using only a few minutes of data from the real robot.


    Using this approach, the robot learns to imitate various locomotion skills from a dog, including different walking gaits, such as pacing and trotting, as well as an agile spinning motion.

    In addition to imitating motions from real dogs, it is also possible to imitate artist-animated keyframe motions, including a dynamic hop-turn:


    In “Learning to Walk in the Real World with Minimal Human Effort”, we developed an automated learning system with software and hardware components, using a multi-task learning procedure, a safety-constrained learner, and several carefully designed hardware and software components. Multi-task learning prevents the robot from leaving the training area by generating a learning schedule that drives the robot towards the center of the workspace. We also reduce the number of falls by designing a safety constraint, which we solve with dual gradient descent.


    This framework successfully trains policies from scratch to walk in different directions without any human intervention.

    Once trained, it is possible to steer the robot with a remote controller. Notice how it's possible to command the robot to turn in place using the controller. This action would be difficult to manually design due to the planar leg structure of the robot, but is discovered automatically using our automated multi-instance learner.