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Human-aware Robo.cs 1 CSE 591: Human-aware Robotics Instructor: Dr. Yu (“Tony”) Zhang Location & Times: CAVC 359, Tue/Thu, 9:00--10:15 AM Office Hours: BYENG 558, Tue/Thu, 10:30--11:30AM Oct 6/Nov 1, 2016 This set of slides borrow from various online sources; it is used for educational purposes only. Slides adapted from Pieter Abbeel (UC Berkeley)

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Page 1: CSE 591: Human-aware Robotics - Arizona State …Human-aware Robo.cs Human modeling Human teammate Human-aware planner Observaons Human models Robot models Plan generaon 8 Modeling

Human-awareRobo.cs

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CSE 591: Human-aware Robotics

Instructor: Dr. Yu (“Tony”) Zhang

Location & Times: CAVC 359, Tue/Thu, 9:00--10:15 AM Office Hours: BYENG 558, Tue/Thu, 10:30--11:30AM

Oct 6/Nov 1, 2016

This set of slides borrow from various online sources; it is used for educational purposes only.

SlidesadaptedfromPieterAbbeel(UCBerkeley)

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Modeling of Humans

Behavior model

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Goal

Modeling of Humans

GoalBehavior model

Ø Goal and intent selection

Goal

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Goal

Modeling of Humans

Goal

river

GoalBehavior model

•  Goal and intent selection Ø  Plan selection

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Goal

Modeling of Humans

Goal

river

GoalBehavior model

•  Goal and intent selection •  Plan selection (informed by the capabilities, and influenced by mental states and etc.)

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Modeling of Humans

Behavior model

•  Goal and intent selection •  Plan selection (informed by the capabilities, and influenced by mental states and etc.)

Ø  Goal/planrecogni?onshouldbeinformedbythebehaviormodel

Ø  Howshouldwelearnabehaviormodel?

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Outline

BehaviormodelingØ Capabilitymodel

GoalpreferenceØ  InverseRL

•  WhyIRL•  InverseRLvs.Behavioralcloning•  Mathema?calformula?onofIRL•  Applica?ons

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Human-awareRobo.cs

Humanmodeling

Humanteammate

Human-awareplanner

Observa?ons

Humanmodels

Robotmodels

Plangenera6on

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Modeling of Humans 1.   Nopre-specifiedgoals/plans2.   Incompleteobserva6ons

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LearningChallenges

CompleteObserva6ons

ActualObserva6ons

Observa6ons(par6al)withindefinitegaps

Behavior model

1.   Nopre-specifiedgoals/plans2.   Incomplete&noisyobserva6ons

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Human-awareRobo.csCapability

->:denoteanatomicstatechange

{has_water(AG),has_coffee_beans(AG)}->{has_boilling_water(AG),has_coffee_beans(AG)}->{has_boilling_water(AG),has_ground_coffee_beans(AG)}->{has_coffee(AG)}

Westartwithanincompleterepresenta6on

§  DEFINITION(CAPABILITY)–Givenanagent,acapabilityisamapping,whichisanasser.onabouttheprobabilityoftheexistenceofaplaninfewerthanorequaltoTatomicstatechangesthatcanconnectthetwostates.

Par6alstates

has_water(AG)=>has_ground_coffee_beans(AG)has_boiling_water(AG)=>has_coffee(AG)…WhenT=2

WhenT=3…(includingallcapabili?eswhenT=2)has_water(AG)=>has_coffee(AG)

Boundonthegapsbetweenobserva6ons

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Human-awareRobo.csCapabilityModel

Capabilitymodelencodesallcapabili6esforagivenT

T-gapcapabilitymodel

Synchroniclinks

Diachroniclinks

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CapabilityModel

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Human-awareRobo.csCapabilityModel&EncodedCapabili?es

sI=>sE

Acondi6onalprobability(specifiedbyapar6alini6alandeventualstate)

Jointdistribu6onoverT

Acapability:T-gapcapabilitymodel

Acapabilitymodelencodesthefollowingdistribu6ons:

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Human-awareRobo.csLearningCapabilityModels

§  Learningmodelstructure

Causalrela?onships(diachroniclinks);variablecorrela?ons(synchroniclinks)

§  Learningmodelparameters Condi?onalprobabili?es

Learningfrom(gap-bounded)plantraces

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ParameterLearning

Learningfromincompletetraces

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ParameterLearning

LearningsamplesApplyBayesianlearning(assumingbetadistribu6ons):

Weassumethatthemaximumnumberofmissingstateobserva6onsbetweenanytwoobserva6onsinthepar6alplantraceisupperboundedbyT

DEFINITION(T-GAPPARTIALPLANTRACE).AT-gappar.alplantraceisapar.alplantraceinwhichallk[1,2…]<=T

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UsingCapabilityModels

§  Robotcanpredictthehuman’snextac?onoutcomes

Statepredic6on(goalrecogni6on)

Proac6veassistance(toincreasegoalsuccessprobability)

§  Robotcanreasonabouthowlikelyataskcanbeachievedbythehuman

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Outline

BehaviormodelingØ Capabilitymodel

GoalpreferenceØ  InverseRL

•  WhyIRL•  InverseRLvs.Behavioralcloning•  Mathema?calformula?onofIRL•  Applica?ons

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Goal

Modeling of Humans

Goal

Goal

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20 Safety?Time?Comfort?Wai?ng?me?Speed?

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Outline

BehaviormodelingØ Capabilitymodel

GoalpreferenceØ  InverseRL

•  WhyIRL•  InverseRLvs.Behavioralcloning•  Mathema?calformula?onofIRL•  Applica?ons

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Reward:R(s)Decayingfactor:Policy:π

MarkovDecisionProcess

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Outline

BehaviormodelingØ Capabilitymodel

GoalpreferenceØ  InverseRL

•  WhyIRL•  InverseRLvs.Behavioralcloning•  Mathema?calformula?onofIRL•  Applica?ons

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Outline

BehaviormodelingØ Capabilitymodel

GoalpreferenceØ  InverseRL

•  WhyIRL•  InverseRLvs.Behavioralcloning•  Mathema?calformula?onofIRL•  Applica?ons

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Directlycomputeapolicy!

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[Abbeel&Ng,2004]

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Outline

BehaviormodelingØ Capabilitymodel

GoalpreferenceØ  InverseRL

•  WhyIRL•  InverseRLvs.Behavioralcloning•  Mathema?calformula?onofIRL•  Applica?ons

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Outline

BehaviormodelingØ Capabilitymodel

GoalpreferenceØ  InverseRL

•  WhyIRL•  InverseRLvs.Behavioralcloning•  Mathema?calformula?onofIRL•  Applica?ons


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