a relational representation for procedural task knowledge

LABORATORY FOR PERCEPTUAL ROBOTICS • UNIVERSITY OF MASSACHUSETTS AMHERST • DEPARTMENT OF COMPUTER SCIENCE

A Relational Representation for Procedural Task

Knowledge

Stephen Hart Roderic Grupen David Jensen

Laboratory for Perceptual RoboticsUniversity of Massachusetts Amherst

New England Manipulation SymposiumMay 25, 2005


Introduction and Motivation

• Robots performing tasks in real-world environments require methods to:• Produce fault-tolerant behavior• Focus on most salient and relevant information • Handle multi-modal, continuous data • Leverage past experience (i.e. adapt and reuse)

• Can we learn probability estimates regarding the effects of sensorimotor variables on task success?– e.g. If I take these actions, how likely am I to succeed at my

task?


Generalized Task Expertise

• Declarative knowledge– Captures abstract knowledge about the task– e.g. find an object, reach to it, pick it up...

• Procedural knowledge– Captures knowledge about how to instantiate the

abstract policy in a particular environmental context– e.g. turn my head to the left, use my left hand to

reach, use an enveloping grasp...


Schema Theory• Arbib (1995) describes control programs composed of:

– Perceptual schema - a Ball might be characterized by “size,” “color,” “velocity,” etc.

– Motor schema - actions characterized by a “degree of readiness” and “activity level.”

• Are such distinctions misleading?– Gibsonian Affordances: a perceptual feature is only meaningful if it

facilitates action – Mirror Neurons: the same neurons will activate when performing an

action or when observing someone else perform that action

• Claim: All perceptual information can come from appropriately designed controllers


How do we learn procedural structure?

• We would like the robot to differentiate its actions based on environmental context– e.g. Pick and Place

• Which available sensorimotor features are correlated – structure learning

• How these features relate, probabilistically, to each other – parameter learning


Relational Data• Data with complex dependencies between instances or varying

structure (not i.i.d.)

• Applicable to robotics domain because:– Different training episodes may exhibit varying structure

• Data designated as Objects and Attributes– Objects are related through the structure of the data– Attributes are related through learned statistical dependencies

• Relational Dependency Networks– approximate the full joint distribution of a set of variables with a set

of conditional probability distributions– Perform Gibbs sampling to do joint inference


locale

bounding box

dimensions

orientation

convergence

state

lift-able

fingers

Localize Reach Grasp

convergence

state

Some Controller Objects


What is Relational About this Data?

ReachController

GraspController

ReachController

Simple Assembly 1:

GraspController

AssembleController



ReachController

GraspController

ReachController

Simple Assembly 2:

GraspController

AssembleController

RemanipulateController


Gathering the Dataset

• Observe an autonomous program or a teleoperator performing a task a variety of ways

• Each trial may follow a different trajectory

• Data is collected after each trial

• Model is learned with Proximityhttp://kdl.cs.umass.edu/proximity/


Experiments

• PickUp with DexterTM

• 2 objects (3 orientations)• tall box, coffee can

• 2 grasps: • 2 VF, 3 VF

• 2 reaches:• top approach• side approach

• 8 locales• uniformly distributed


locale

bounding box

dimensions

orientation

convergence

state

lift-able

fingers

Localize Reach Grasp

convergence

state

The Learned Model Graph


Attribute Trees

• The RDN algorithm estimates a CPD for each attribute– Learns a locally consistent Relational Probability

Tree (RPT) for that attribute

• Each tree focuses attention on the most salient predictors of the corresponding attribute– Manages complexity– Allows for easy and intuitive interpretation– Each attribute (sensorimotor feature) has an

affordance in terms of the current task


RPT for “Lift-able”


Using the RDN to construct policy

• How do we use the learned schema to perform the task again?– At each action point:

• perform joint inference on task success variables and find most likely resource assignment

• Use this assignment and see how likely success is• Perform next action with resource binding, possibly

uncovering new information through interaction


Yeah, but... how does it perform?

• Pick up the can with 2 or 3 fingers from the top

• Pick up the box with 2 fingers – From the side or the top standing up– From the top laying down

• Predicts little probability of success if object is outside reachable workspace


Where to Next?

• How do we learn the declarative structure?– Previous work by Huber, Platt, etc.

• Capture dynamic response of controllers during execution– Learn dependencies through direct interaction with

the environment

• Can we sample a set attributes from uncountable possible set– Resample if poor policies are learned


The End


RDNs in Robotics• What do we know?

– a collection of controllers are necessary for a task, usually organized as a sequence of sub-goals

– controllers have state, attached resources, and can reveal perceptual information through execution

– controllers can execute sequentially or in conjunction

• What don’t we know?– Which sensorimotor features of each controller are

important and how they correlate


Localize

Reach Reach Grasp Grasp

Localize

Reach Reach Grasp

Localize

Reach Grasp Grasp

Localize

Reach Grasp

Four Training Structures



ReachController

GraspController

LocalizeController

ReachController

ObstacleAvoidanceController

ObstacleAvoidanceController

KinematicConditioning

Controller

Pick and Transport:

Not independently distributed!!!

sequential relations

conjunctive relations

a relational representation for procedural task knowledge

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