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2013.08.21 - Stefan Schaal: From Learning Movement Primitives to Associative Skill Memories


People:

  • Learning, perception and action
    • Machine learning
    • Neuroscience
    • Robots
  • Autonomous robots - how do we make things that work? (Robotic systems, theory/algorithms, neuro studies)
    • Modular planning
    • Teaching and imitation
    • Trial and error learning
    • Interaction with the environment
    • Learning control
    • Multi-modal perception
  • Movement primitives
    • Control policy
    • Dynamic movement primitives are solved by producing attractor/repeller dynamics
  • Trial error (reinforcement learning)
    • Algorithm
      • Teach with imitation
      • Provide an objective function
      • Perform trial and error to explore space
    • Reinforcement learning
      • Policy
      • Reward function
      • Value function
      • Model
    • What do people do?
      • State-based
      • Trajectory-based
        • Scalable to high-dimensions
    • Path-integral Reinforcement Learning
      • Take affine system dynamics
      • Have stochasitc cost function
      • Try and solve stochastic HJB
        • Use magic log-transform trick
          • Squash reward into weight from zero-to-one, apply weight
        • Apply feynman-kac theorem (allows empirical evaluation)
        • Generate optimal control policy
    • Adapting to dynamically changing world
      • Hoffmann ICRA 2009
      • Pastor IROS 2011
  • Learning control with internal models
    • Compliant control
  • Associative skill memories
    • Action is associated with all perceptual information
      • Success vs failure detection based on associating sensory info to actions that allow prediction of next action
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