Melvin's digital garden

Learning to Walk through Imitation

CREATED: 200711070834 Speaker: Rawichote Chalodhorn WWW:

** Learning to Walk Through Imitation

  • motivation: humanoid robots in RoboCup
  • hand tuned motions is timed consuming, depends on specific robots
  • learning from motion capture ** differences between human and robot model ** needs to be optimized to match dynamics of robot ** direct optimization of high dimensional data is intractable ** dynamic model of robot not available

** Approach

  • reduce the number of dimensions using dimension reduction techniques
  • further reduce the amount of data by modeling the points in the posture subspace as a loop in cylindrical coordinate system
  • learn a model of the gyroscope signals which indicate stability of the robot, using a time delay RBF network
  • posture command + history of gyroscope signals -> new gyroscope signals at time t
  • optimize the posture to minimize the gyroscope signal (instability)
  • may need additional iterations to provide feedback to improve the signal predictor model
  • motion scaling: scale down motion capture information to reduce the amount of movement, learn the motions from scaled down motion capture data first and scale up gradually
  • does not need dynamic model, can be applied to other robots

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