Laboratory for Perceptual Robotics – Department of Computer Science Hierarchical Mechanisms for Robot Programming Shiraj Sen Stephen Hart Rod Grupen Laboratory for Perceptual Robotics University of Massachusetts Amherst May 30, 2008 NEMS ‘08

2 Laboratory for Perceptual Robotics – Department of Computer Science Outline Hierarchical mechanisms for robot programming representation programming Action Potential functions Value functions State representation user defined reinforcement learning intrinsic extrinsic

3 Laboratory for Perceptual Robotics – Department of Computer Science Hierarchical Actions Σ G H Σ G H Σ G H force velocity references feedback signals ϕ potential fields Φ value functions greedy traversal avoids local minimum programs closed loop primitive actions

4 Laboratory for Perceptual Robotics – Department of Computer Science Primitive Action Programming Interface Sensory Error ()  Visual ( u ref )  Tactile ( f ref )  Configuration variables ( θ ref )  Operational Space( x ref ) Potential Functions ()  Spring potential fields ( ϕ h )  Collision-free motion fields ( ϕ c )  Kinematic conditioning fields ( ϕ cond ) Motor Variables () Subsets of :  Configuration Variables  Operational Space Variables primitive actions: a = Nullspace Projection a 1 a 2

5 Laboratory for Perceptual Robotics – Department of Computer Science State Representation  Discrete abstraction of action dynamics.  4-level logic in control predicate p i no reference (  ) convergence unknown X descending gradient

6 Laboratory for Perceptual Robotics – Department of Computer Science Hierarchical Programming  A program is defined as a MDP over a vector of controller predicates: S =  p 1 … p N   Absorbing states in the value function capture “convergence” of programs. X  Learn value functions using reinforcement learning

7 Laboratory for Perceptual Robotics – Department of Computer Science Stack Insert Grasp Touch Catalog Intrinsic Reward  Goal: build deep control knowledge  Reward controllable interaction with the world controllers with direct feedback from the external world. Track X convergence event X - 1 0

8 Laboratory for Perceptual Robotics – Department of Computer Science Experimental Demonstration  Motor units Two 7-DOF Barrett WAMs Two 4-DOF Barrett Hands 2-DOF pan/tilt stereo head  Sensory feedback Visual Hue Saturation Intensity Texture Tactile 6-axis finger-tip F/T sensors Proprioceptive Dexter

9 Laboratory for Perceptual Robotics – Department of Computer Science STAGE 1: SaccadeTrack - 25 Learning Episodes a track a saccade X 1 X 0 1 X 0 X X - X S st =  p saccade p track  rewarding action Track-saturation

10 Laboratory for Perceptual Robotics – Department of Computer Science S rg =  p st p reach p grab  STAGE 2: ReachGrab - 25 Learning Episodes rewarding action Touch Track-saturation

11 Laboratory for Perceptual Robotics – Department of Computer Science STAGE 2: ReachGrab - 25 Learning Episodes Touch Track-saturation

12 Laboratory for Perceptual Robotics – Department of Computer Science STAGE 3: VisualInspect - 25 Learning Episodes S vi =  p rg p cond p track(blue)  Touch Track-saturation Track-blue rewarding action

13 Laboratory for Perceptual Robotics – Department of Computer Science STAGE 3: VisualInspect - 25 Learning Episodes Touch Track-saturation Track-blue

14 Laboratory for Perceptual Robotics – Department of Computer Science STAGE 4: Grasp – User Defined Reward X X X X X X ReachGrab X X 0 0 X 1 1 X 1 0 X 0 1 a moment a force Touch Track-saturation Grasp Track-blue S grasp =  p rg p moment p force  rewarding action

15 Laboratory for Perceptual Robotics – Department of Computer Science STAGE 5: PickAndPlace – User Defined Reward a transport a moment X X X X Grasp X 0 - X 0 0 X X X X 1 1 X 1 0 S pnp =  p g p transport p moment  rewarding action

16 Laboratory for Perceptual Robotics – Department of Computer Science Conclusions  Mechanisms for creating hierarchical programs. recursive formulation of potential functions and value functions.  control theoretic representation for action, state, and intrinsic reward.  Experimental demonstration of programming manipulation skills using staged learning episodes.  Intrinsic reward pushes out new behavior and models the affordances of objects.

17 Laboratory for Perceptual Robotics – Department of Computer Science Thank You