1. Laboratory for Perceptual Robotics Department of Computer Science University of Massachusetts Amherst Natural Task Decomposition with Intrinsic Potential Fields Steve Hart Emily Horrell Shichao Ou Shiraj Sen John Sweeney Rod Grupen Third Annual New England Manipulation Symposium Rensselaer Polytechnic Institute June 1, 2007

2. Overview Control Decomposition –Intrinsic Potential Fields –The Control Basis Architecture –New Programming API What’s Next? –Multi-objective behavior –Generalization and Transfer

3. Potential Fields - move-to - visual track - wrench-closure (grasp) Extrinsic Navigation Functions Postural Bias - (range of motion) Kinematic Conditioning Intrinsic Potential Fields

4. Kinematic Conditioning There are various traditional conditioning metrics for kinematic transformations. Measure of ManipulabilityMeasure of Localizability Volume of “Conditioning Ellipsoid”

5. The Control Basis Combinatoric framework for sensorimotor control  : control function  : sensor  : effector resource model  ( ,  ) : controller : null space combinations  : sequential programs (schema) ∆  2  1 Control-based state/action space reflects the convergence status of a family of controllers

6. Dexter The UMass BiManual Robot Two 7-DOF Barrett Technology Whole-Arm Manipulators 2-DOF pan/tilt stereo head Two 3-Finger Hands with 6-axis load cell fingertip sensors

7. Control Basis API

8. Visual Inspection

9. Conditioned Grasping

10. Classification Through Visual Conditioning

11. An Anticipated Developmental Programming Trajectory Stage 2: touch what you see Stage 3: learning “out of range” Stage 1: learning about objects learning about objects: stacking seriation humans

12. s0s0 s1s1 s2s2 s3s3 a0a0 a1a1 a2a2 Schema Abstraction procedural (how) declarative (what) a i =  i ( i,  i )

13. procedural (how) declarative (what) s0s0 s1s1 s2s2 s3s3 a0a0 a1a1 a2a2 S0S0 S1S1 S2S2 S3S3 00 11 22 procedural declarative (0,0)(0,0)(1,1)(1,1)(2,2)(2,2) a i =  i ( i,  i ) Schema Abstraction

14. s0s0 s1s1 s2s2 s3s3 a0a0 a1a1 a2a2 procedural (how) declarative (what) a i =  i ( i,  i ) (1,1)(1,1)( 1, 1 )’ S0S0 S1S1 S2S2 S3S3 00 11 22 (0,0)(0,0)(2,2)(2,2) declarative defined by context procedural: Schema Abstraction

15. Pick-and-Place Context Variations Object Position = Left Left Arm Reach w/ Right Arm Reach w/ YesNo Object Scale = Large No Both Arms Reach w/ Yes

16. Transfer Learning - bringing prior experience to bear Tele-operator sorting instruction sorting replay w/ prior knowledge (1) parse events to find a matching schema. (2) associate goals with schema (3) Replicate demonstration with contingencies

17. Summary Control Decomposition Control Basis API Schema Learning Generalization/Transfer