Laboratory for Perceptual Robotics

Department of Computer ScienceUniversity of Massachusetts

Amherst

Natural Task Decomposition with Intrinsic Potential

FieldsSteve Hart Emily Horrell Shichao Ou Shiraj Sen John Sweeney Rod Grupen

Third Annual New England Manipulation Symposium

Rensselaer Polytechnic InstituteJune 1, 2007

Overview

• Control Decomposition– Intrinsic Potential Fields – The Control Basis Architecture– New Programming API

• What’s Next?– Multi-objective behavior– Generalization and Transfer

Potential Fields

- move-to- visual track- wrench-closure (grasp)

Extrinsic Navigation Functions

• Postural Bias- (range of motion)

• Kinematic Conditioning

Intrinsic Potential Fields

Kinematic Conditioning

• There are various traditional conditioning metrics for kinematic transformations.

Measure of Manipulability Measure of Localizability

Volume of “Conditioning Ellipsoid”

The Control Basis

• Combinatoric framework for sensorimotor control

: control function

: sensor

: effectorresourcemodel

(, ) : controller

: null space combinations

: sequential programs (schema)

∆2 1

• Control-based state/action space reflects the convergence status of a family of controllers

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

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Control Basis API

Visual Inspection

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Conditioned Grasping

Classification Through Visual Conditioning

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

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s0 s1 s2 s3a0 a1 a2

Schema Abstraction

procedural (how)

declarative (what)ai = i(i,i)

procedural (how)

declarative (what)s0 s1 s2 s3

a0 a1 a2

S0 S1 S2 S30 1 2

procedural

declarative

(0,0) (1,1) (2,2)

ai = i(i,i)

Schema Abstraction

s0 s1 s2 s3a0 a1 a2

procedural (how)

declarative (what)ai = i(i,i)

(1,1) (1,1)’

S0 S1 S2 S30 1 2

(0,0) (2,2)

declarative

defined by contextprocedural:

Schema Abstraction

Pick-and-Place Context Variations

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Object Position= Left

LeftArmReach w/

RightArm Reach w/

Yes No

Object Scale= Large

No

Both ArmsReach w/

Yes

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 goalswith schema

(3) Replicatedemonstrationwith contingencies

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Summary

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• Control Decomposition

• Control Basis API

• Schema Learning

• Generalization/Transfer