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Adaptive Robotics COM2110 Autumn Semester 2008 Lecturer: Amanda Sharkey “Robot” the word “robot” comes from the play `Rossum`s Universal Robots`, by Czech writer Karel Capek (1921) Robot, from robota, “servitude, forced labour, drudgery” Robots rebel, and kill all humans What is a robot? Joseph Engelberger, a pioneer in industrial robotics: "I can't define a robot, but I know one when I see one." Brady (1985) “the intelligent connection of perception to action” Arkin (1998) “An intelligent robot is a machine able to extract information from its environment and use knowledge about its world to move safely in a meaningful and purposive manner” Robotics Industry Association: “a robot is a re-programmable, multifunctional, manipulator designed to move material, parts, tools or specialised devices through variable programmed motions for the performance of a variety of tasks” (excludes mobile robots!) Changing definitions “Stop fearing the robot – stop making a man of him! Just remember that the sewing machine is a robot, the automobile is a robot, the electric car and the phonograph and the telephone are all robots. Each one men have developed in order to unburden themselves of some onerous task and on to better things. Each one does a specific job, and no more. Why begin now to worry about robots when we have been enjoying their services for centuries?” Woodbury, D. (1927) Dramatising the “robot”, New York Times, Nov 1st Like Wittgenstein and “games” No single feature shared by the many examples, but rather “a complicated network of similarities, overlapping and criss-crossing” [Wittgenstein, 1953]. The same is also true of ‘robot’ – the various examples bear family resemblances rather than a single meaning. Different groups of robots Autonomous robots Industrial robots Human-like robots Self-configurable robots Biological models Toys and companions Course Aims To present the key concepts of a recent approach to AI To consider the underlying mechanisms for robot control And contrast to earlier approaches To inform about research in robotics What are the motivations? Applications Biological inspiration Biorobotic modelling Understanding intelligence Teaching Method Lectures, and assignment. See website for course (Lecturer’s module pages) Assessment: Exam and assignment Background Reading Clark, A. (1997) Being There: Putting Brain, Body and World Together Again. A Bradford Book, MIT Press Franklin, S. (1995) Artificial Minds: A Bradford Book, MIT Press Nolfi, S. and Floreano, D. (2000) Evolutionary Robotics: The biology, intelligence and technology of self-organising machines. A Bradford Book, MIT Press Pfeifer, R., and Scheier, C. (2001) Understanding Intelligence, MIT Press Why robotics? Can we create artificial beings? Are we machines? How do we work? Understanding by building Making robots to perform useful tasks Robots as companions? What is Adaptive Robotics? Recent approach to AI Reflected in • Behaviour based robotics • Reactive robotics • Evolutionary robotics • Artificial Life • Swarm Intelligence and swarm robotics • Embodied cognition Different views of mind and cognition Emphasis on Mind and Reasoning independent of world (computationalism) How can mind emerge from the workings of a physical machine? (brain) (connectionism) Relationship between brain, body, mind and world…. (embodied cognition) Three Stage Progression to current emphasis on Embodied Cognition 1. 2. 3. Classical Cognitivism or computationalism (late 1950’s to 1980’s) Connectionism (main period– 1980’s) Embodied Cognition and Adaptive Intelligence (1990’s to present) N.B. dates only a rough guide 1. Computationalism Mental states = computational states Good Old Fashioned Artificial Intelligence GOFAI Physical Symbol System Hypothesis (Newell and Simon, 1976) A physical symbol system is a necessary and sufficient condition for general intelligent action. intelligence is symbol manipulation computers manipulate symbols computers can be intelligent 1. Computationalism cont. Memory as retrieval from stored symbolic database Problem solving as logical inference Cognition as centralised Environment just a problem domain Body as an input device Shakey Functionalism “The mind is to the brain as the program is to the hardware” (Johnson-Laird, 1988) - hardware/software distinction - we are interested in the software – could run on any hardware (Swiss cheese?) 2. Connectionism Neural nets An account of mental states in terms of neurons – related to brain Memory as pattern recreation Problem solving as pattern completion and transformation Cognition – decentralised 3. Embodied Cognition As connectionism PLUS Environment as active resource Body as part of computational loop Brain, body, world intricately interconnected 3. Embodied cognition cont. Gradual move away from anthropocentric view Greater awareness of abilities of non-human organisms, and their abilities to interact with and survive in the world. Early mobile robots: Shakey Shakey the Robot Developed by SRI (Stanford Research Institute) from 1966-1972 First mobile robot to visually interpret, and reason about its surroundings TV camera, range finder, bump sensors Programs for sensing, modelling and planning Example task: “push the block off the platform” Stanford Cart TV cameras: took pictures of scenes, and planned path between obstacles Sense Model Plan Action Brooks:1991 “Intelligence without representation” Realisation that mobility, vision and ability to survive are important aspects of intelligence Brooks and idea of Creatures Able to cope with changing and uncertain world Should have goals, and purpose in being “An ant, viewed as a behaving system, is quite simple. The apparent complexity of its behavior over time is largely a reflection of the complexity of the environment in which it finds itself” Herbert A. Simon, 1969 Idea of reactive responses to the world, instead of modelling and planning. Intelligence is determined by the dynamics of interaction with the world. Key concepts in new approach to AI a) Reactive behaviour b) Adaptivity c) Situatedness d) Embodiment e) Emergence and Self-organisation Changing view of intelligence a. Reactive Intelligence Arkin (1995): hallmark characteristics - emphasis on behaviours and simple sensorimotor pairings - Avoidance of abstract representational knowledge (time consuming) - Animal models of behaviour - Demonstrable results: walking robots, pipe-crawling robots, military robots etc. Reactivity Biological inspiration: e.g. birds flocking, ants foraging. Sufficiency Grey Walter (1953) electronic tortoise. Braitenberg (1984) synthetic psychology Brooks (1986) behaviour-based robotics and subsumption architecture. b. Adaptivity Adaptivity: ability to adjust oneself to the environment Physiological adaptation – e.g. sweating to adjust to heat Evolutionary adaptation – e.g. peppered moth. Light in colour, in industrial area became dark in colour Sensory adaptation – e.g. our pupils adjusting to poor light Adaptation by learning – e.g. where food is found c. Situated An emphasis on robot’s interaction with its environment (related to embodiment) Brooks (1991) “the world is its own best model” A situated agent must respond in a timely fashion to its inputs. d. Embodiment Physical grounding of robot in the world Brooks (1991): embodiment of intelligent systems critical because Only an embodied intelligent agent is fully validated as one that can deal with the real world Only through physical grounding can any meaning be given to the processing occurring within the agent “Intelligence is determined by the dynamics of interaction with the world” (Brooks 1991) - embodied cognition - - A solution to the symbol grounding problem? (remember Searle’s Chinese Room!) e. Emergence Adaptive success that emerges from complex interactions between body, world and brain A non-centrally controlled (or designed) behaviour that results from the interactions of multiple simple components Meanings of the term ‘emergence’ Surprising situations or behaviours Property of system not contained in any of its parts Behaviour resulting from agentenvironment interaction that is not explicitly programmed. Ant colony Individual ants are simple and reactive (?) Emergent behaviour of colony is sophisticated Self-organisation An ant colony is self-organised – simple individuals, local interactions, emergent behaviour .. No global control “self-organisation is a set of dynamical mechanisms whereby structures appear at the global level of a system from interactions among its lower-level components. The rules specifying the interactions among the system’s constituent units are executed on the basis of purely local information, without reference to the global pattern, which is an emergent property of the system rather than a property imposed upon the system by an external ordering influence” (Bonabeau, Dorigo and Theraulaz, 1999) Frisbee collecting robots Robots in an arena + frisbees Simple rules Emergent result – clustering and sorting of frisbees. Changing view of intelligence GOFAI – emphasis on reasoning, planning, and representation. Human-centred (anthropocentric) Behaviour-based robotics and beyond: emphasis on simpler organisms and their ability to survive in the world. Reading: For this week and next. Brooks, R.A. (1991) Intelligence without Reason. Proceedings of 1991 International Joint Conference on Artificial Intelligence, 569-595. Robots in the news Murata Manufacturing: Murata boy – controlled by blue tooth, and can ride a bike forwards and backwards. 9/25/2008 12:23 PM ET iRobot Corp. (IRBT: News ), on Thursday, said that it has received an additional $13.3 million order from the US Army for PackBot 510 with FasTac Kit robots for carrying bomb identification and other life-threatening missions.