Research at the Boundary of Robotics and AI Prof: Peter Stone - PowerPoint PPT Presentation

Research at the Boundary of Robotics and AI Prof: Peter Stone Department of Computer Science The University of Texas at Austin

AI and Robotics • Challenge problems Peter Stone

AI and Robotics • Challenge problems • Always on robots (in a human-occupied space) Peter Stone

AI and Robotics • Challenge problems • Always on robots (in a human-occupied space) • Ad hoc teamwork Peter Stone

AI and Robotics • Challenge problems • Always on robots (in a human-occupied space) • Ad hoc teamwork • Our role in a climate where industry is interested Peter Stone

A Goal of AI and Robotics Robust, fully autonomous agents in the real world Peter Stone

A Goal of AI and Robotics Robust, fully autonomous agents in the real world How? Peter Stone

A Goal of AI and Robotics Robust, fully autonomous agents in the real world How? • Build complete solutions to relevant challenge tasks Peter Stone

A Goal of AI and Robotics Robust, fully autonomous agents in the real world How? • Build complete solutions to relevant challenge tasks • Drives research on component algorithms, theory Peter Stone

A Goal of AI and Robotics Robust, fully autonomous agents in the real world How? • Build complete solutions to relevant challenge tasks • Drives research on component algorithms, theory • A top-down, empirical approach Peter Stone

A Goal of AI and Robotics Robust, fully autonomous agents in the real world How? • Build complete solutions to relevant challenge tasks • Drives research on component algorithms, theory • A top-down, empirical approach “Good problems . . . produce good science” [ Cohen, ’04 ] Peter Stone

Bottom-Up Metaphors Russell, ’95 “Theoreticians can produce the AI equivalent of bricks, beams, and mortar with which AI architects can build the equivalent of cathedrals.” Peter Stone

Bottom-Up Metaphors Russell, ’95 “Theoreticians can produce the AI equivalent of bricks, beams, and mortar with which AI architects can build the equivalent of cathedrals.” Koller, ’01 “In AI . . . we have the tendency to divide a problem into well-defined pieces, and make progress on each one. Peter Stone

Bottom-Up Metaphors Russell, ’95 “Theoreticians can produce the AI equivalent of bricks, beams, and mortar with which AI architects can build the equivalent of cathedrals.” Koller, ’01 “In AI . . . we have the tendency to divide a problem into well-defined pieces, and make progress on each one. . . . Part of our solution to the AI problem must involve building bridges between the pieces.” Peter Stone

Dividing the Problem AI Robotics Vision Learning Natural Language Game Multiagent Distributed Knowledge Theory Reasoning Optimization Representation Peter Stone

The Bricks Robotics Vision Learning Natural Language Game Multiagent Distributed Knowledge Theory Reasoning Optimization Representation Peter Stone

The Beams and Mortar Robotics Vision Learning Natural Language Game Multiagent Distributed Knowledge Theory Reasoning Optimization Representation Peter Stone

Towards a Cathedral? ? Robotics Vision Learning Natural Language Game Multiagent Distributed Knowledge Theory Reasoning Optimization Representation Peter Stone

Or Something Else? ? Robotics Vision Learning Natural Language Game Multiagent Distributed Knowledge Theory Reasoning Optimization Representation Peter Stone

A Different Problem Division AI Peter Stone

Top-Down Approach Robotics Vision Learning Natural Language Game Multiagent Distributed Knowledge Theory Reasoning Optimization Representation “Good problems . . . produce good science” [ Cohen, ’04 ] Peter Stone

Meeting in the Middle Robotics Vision Learning Natural Language Game Multiagent Distributed Knowledge Theory Reasoning Optimization Representation Peter Stone

Meeting in the Middle Robotics Vision Learning Natural Language Game Multiagent Distributed Knowledge Theory Reasoning Optimization Representation Top-down approaches underrepresented Peter Stone

Choosing the Challenge • Features of good challenges: [ Cohen, ’04 ] − Frequent tests; Graduated series of challenges − Accept poor performance; Complete agents Peter Stone

Choosing the Challenge • Features of good challenges: [ Cohen, ’04 ] − Frequent tests; Graduated series of challenges − Accept poor performance; Complete agents • Closed loop + specific goal • 50-year technical, scientific goals − Beyond commercial applications — not possible now − Moore’s law not enough Peter Stone

Choosing the Challenge • Features of good challenges: [ Cohen, ’04 ] − Frequent tests; Graduated series of challenges − Accept poor performance; Complete agents • Closed loop + specific goal • 50-year technical, scientific goals − Beyond commercial applications — not possible now − Moore’s law not enough • There are many — choose one that inspires you Peter Stone

Good Problems Produce Good Science Manned flight Apollo mission Manhattan project RoboCup soccer Goal: By the year 2050, a team of humanoid robots that can beat the human World Cup champion team. [ Kitano, ’97 ] Peter Stone

RoboCup Soccer • Still in progress • Many virtues: − Incremental challenges, closed loop at each stage − Robot design to multi-robot systems − Relatively easy entry − Inspiring to many • Visible progress Peter Stone

AI and Robotics • Challenge problems • Always on robots (in a human-occupied space) • Ad hoc teamwork • Our role in a climate where industry is interested Peter Stone

Teamwork Peter Stone

Teamwork Peter Stone

Teamwork • Typical scenario: pre-coordination − People practice together − Robots given coordination languages, protocols − “Locker room agreement” [Stone & Veloso, ’99] Peter Stone

Ad Hoc Teams • Ad hoc team player is an individual − Unknown teammates (programmed by others) Peter Stone

Ad Hoc Teams • Ad hoc team player is an individual − Unknown teammates (programmed by others) • Teammates likely sub-optimal: no control Peter Stone

Ad Hoc Teams • Ad hoc team player is an individual − Unknown teammates (programmed by others) • Teammates likely sub-optimal: no control Peter Stone

Ad Hoc Teams • Ad hoc team player is an individual − Unknown teammates (programmed by others) • Teammates likely sub-optimal: no control Goal: Create a good team player Peter Stone

Ad Hoc Teams • Ad hoc team player is an individual − Unknown teammates (programmed by others) • Teammates likely sub-optimal: no control Goal: Create a good team player • Introduced as AAAI Challenge Problem [ Stone et al. ’10 ] Peter Stone

Ad Hoc Teams • Ad hoc team player is an individual − Unknown teammates (programmed by others) • Teammates likely sub-optimal: no control Goal: Create a good team player • Introduced as AAAI Challenge Problem [ Stone et al. ’10 ] − Theory: repeated games, bandits [ AIJ, ’11 ] Peter Stone

Ad Hoc Teams • Ad hoc team player is an individual − Unknown teammates (programmed by others) • Teammates likely sub-optimal: no control Goal: Create a good team player • Introduced as AAAI Challenge Problem [ Stone et al. ’10 ] − Theory: repeated games, bandits [ AIJ, ’11 ] − Experiments: pursuit , flocking [ Barrett, Genter, ’12 ] Peter Stone

Ad Hoc Teams • Ad hoc team player is an individual − Unknown teammates (programmed by others) • Teammates likely sub-optimal: no control Goal: Create a good team player • Introduced as AAAI Challenge Problem [ Stone et al. ’10 ] − Theory: repeated games, bandits [ AIJ, ’11 ] − Experiments: pursuit , flocking [ Barrett, Genter, ’12 ] − RoboCup experiments; Peter Stone

Ad Hoc Teams • Ad hoc team player is an individual − Unknown teammates (programmed by others) • Teammates likely sub-optimal: no control Goal: Create a good team player • Introduced as AAAI Challenge Problem [ Stone et al. ’10 ] − Theory: repeated games, bandits [ AIJ, ’11 ] − Experiments: pursuit , flocking [ Barrett, Genter, ’12 ] − RoboCup experiments; AAAI Workshops Peter Stone

AI and Robotics • Challenge problems • Always on robots (in a human-occupied space) • Ad hoc teamwork • Our role in a climate where industry is interested Peter Stone