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Talking to Robots Speech-Directed Motion Planning Anil Venkatesh SUNFEST 2008

Talking to Robots - University of Pennsylvania · 2018. 7. 17. · Talking to Robots Speech-Directed Motion Planning Anil Venkatesh SUNFEST 2008. Project Goals • Economy of spoken

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Page 1: Talking to Robots - University of Pennsylvania · 2018. 7. 17. · Talking to Robots Speech-Directed Motion Planning Anil Venkatesh SUNFEST 2008. Project Goals • Economy of spoken

Talking to Robots

Speech-Directed Motion Planning

Anil VenkateshSUNFEST 2008

Page 2: Talking to Robots - University of Pennsylvania · 2018. 7. 17. · Talking to Robots Speech-Directed Motion Planning Anil Venkatesh SUNFEST 2008. Project Goals • Economy of spoken

Project Goals

• Economy of spoken commands• Customization with little programming

Page 3: Talking to Robots - University of Pennsylvania · 2018. 7. 17. · Talking to Robots Speech-Directed Motion Planning Anil Venkatesh SUNFEST 2008. Project Goals • Economy of spoken

• Spoken Language Understanding Shell (SLUSH)

• Interactive language model• No training or learning

Page 4: Talking to Robots - University of Pennsylvania · 2018. 7. 17. · Talking to Robots Speech-Directed Motion Planning Anil Venkatesh SUNFEST 2008. Project Goals • Economy of spoken

What SLUSH does

• Most Likely Sequence (MLS) output every 10ms

[GO,den.office]STA/N_PP;[IDENT,e0]N/den;;/N_SIL? 5

_ 1 2 _

Page 5: Talking to Robots - University of Pennsylvania · 2018. 7. 17. · Talking to Robots Speech-Directed Motion Planning Anil Venkatesh SUNFEST 2008. Project Goals • Economy of spoken

Linear Temporal Logic

ϕ ϕ ϕ

ϕϕ1 ϕ2

True if is true in the next stateϕ

True if is true until becomes trueϕ1 ϕ2

ϕϕ

True if is true in some next stateϕ

True if is true in every next stateϕ

Page 6: Talking to Robots - University of Pennsylvania · 2018. 7. 17. · Talking to Robots Speech-Directed Motion Planning Anil Venkatesh SUNFEST 2008. Project Goals • Economy of spoken

• Linear Temporal Logic Motion Planner (LTLMop)

• Structured English => Controller

If you are sensing Sandwich then do Eat

Page 7: Talking to Robots - University of Pennsylvania · 2018. 7. 17. · Talking to Robots Speech-Directed Motion Planning Anil Venkatesh SUNFEST 2008. Project Goals • Economy of spoken

Linking SLUSH and LTLMop

• Grammar and Lexicon files• Pronunciation file• MLS processing

Page 8: Talking to Robots - University of Pennsylvania · 2018. 7. 17. · Talking to Robots Speech-Directed Motion Planning Anil Venkatesh SUNFEST 2008. Project Goals • Economy of spoken

Grammar and Lexicon

Go to the room.

G N -> (ROOM) roomG NP -> the NG Simp -> go to NP (GO)

L GO : (source-set)L ROOM : (set-of-all i in (source-set) s-t ((ilk of i) is (room)))

Page 9: Talking to Robots - University of Pennsylvania · 2018. 7. 17. · Talking to Robots Speech-Directed Motion Planning Anil Venkatesh SUNFEST 2008. Project Goals • Economy of spoken

If-Then grammar and lexicon

If you see the sign, go to the room.

G Sind -> you see NPG PPcond -> if Sind (IF)G Simp -> PPcond SimpG N -> (SIGN) sign

L IF : (context-set)L SIGN : (set-of-all i in (source-set) s-t ((ilk of i) is (sign)))

Page 10: Talking to Robots - University of Pennsylvania · 2018. 7. 17. · Talking to Robots Speech-Directed Motion Planning Anil Venkatesh SUNFEST 2008. Project Goals • Economy of spoken

MLS processing

• Throw away phonetic transitions• Cross-reference in pronunciation file for

words• Extract referents and locations

[GO,e0.e1]STA/N_PP*;[IDENT,e0]N/room;;/R_UW_M_SIL? 2

...[IF-SEE,e12]PPcond/PP_PP*;[INPRIME,e0]PP/sil;;/S_AA_IH_N_SIL? 3

Page 11: Talking to Robots - University of Pennsylvania · 2018. 7. 17. · Talking to Robots Speech-Directed Motion Planning Anil Venkatesh SUNFEST 2008. Project Goals • Economy of spoken

Linking to LTLMop

• Identify the kind of command• Choose appropriate template sentences

1. If you are in Location1 then do Location1Flag2. If you activated Location1Flag and you were not in Location2

then do Location1Flag3. {If you are activating Location1Flag then visit Location2}4. If you were in Location2 do not Location1Flag

If you are in Location1, go to Location2

Page 12: Talking to Robots - University of Pennsylvania · 2018. 7. 17. · Talking to Robots Speech-Directed Motion Planning Anil Venkatesh SUNFEST 2008. Project Goals • Economy of spoken

If you see the alarm in the office,catch the convict in the forest.

If you catch the convict in the forest, lock him up in the jail.

Environment starts with falseRobot starts with false

Visit yardVisit forestVisit officeVisit jailVisit bridge

Environment starts with falseRobot starts with false

Visit yardVisit forestVisit officeVisit jailAlways not bridge

Page 13: Talking to Robots - University of Pennsylvania · 2018. 7. 17. · Talking to Robots Speech-Directed Motion Planning Anil Venkatesh SUNFEST 2008. Project Goals • Economy of spoken

Results

• Expressivity

• Objectives met

IF THENSENSOR [in LOCATION_1] go to LOCATION_2in LOCATION_1 ACTION_2 [in LOCATION_2]ACTION_1 [in LOCATION_1]

Page 14: Talking to Robots - University of Pennsylvania · 2018. 7. 17. · Talking to Robots Speech-Directed Motion Planning Anil Venkatesh SUNFEST 2008. Project Goals • Economy of spoken

Acknowledgments

My advisorsWilliam Schuler (University of Minnesota)The SUNFEST program

Images fromhttp://b-bcs.com/http://mohitdhawan.blogspot.com/2005_04_01_archive.htmlhttp://scary-manilow.livejournal.com/