Artificial Intelligence Instructor: Monica Nicolescu

Artificial Intelligence Outline Introduction Robotics: what it is, what it isn’t, and where it came from Key concepts Brief history Robot control architectures Deliberative control Reactive control Hybrid control Behavior-based control Artificial Intelligence

Artificial Intelligence Key Concepts Situatedness Agents are strongly affected by the environment and deal with its immediate demands (not its abstract models) directly Embodiment Agents have bodies, are strongly constrained by those bodies, and experience the world through those bodies, which have a dynamic with the environment Artificial Intelligence

Artificial Intelligence Key Concepts (cont.) Situated intelligence is an observed property, not necessarily internal to the agent or to a reasoning engine; instead it results from the dynamics of interaction of the agent and environment and behavior are the result of many interactions within the system and w/ the environment, no central source or attribution is possible Artificial Intelligence

Artificial Intelligence What is Robotics? Robotics is the study of robots, autonomous embodied systems interacting with the physical world A robot is an autonomous system which exists in the physical world, can sense its environment and can act on it to achieve some goals Robotics addresses perception, interaction and action, in the physical world Artificial Intelligence

Artificial Intelligence Uncertainty Uncertainty is a key property of existence in the physical world Physical sensors provide limited, noisy, and inaccurate information Physical effectors produce limited, noisy, and inaccurate action The uncertainty of physical sensors and effectors is not well characterized, so robots have no available a priori models Artificial Intelligence

Artificial Intelligence Uncertainty (cont.) A robot cannot accurately know the answers to the following: Where am I? Where are my body parts, are they working, what are they doing? What did I just do? What will happen if I do X? Who/what are you, where are you, what are you doing, etc.?... Artificial Intelligence

Artificial Intelligence The term “robot” Karel Capek’s 1921 play RUR (Rossum’s Universal Robots) It is (most likely) a combination of “rabota” (obligatory work) and “robotnik” (serf) Most real-world robots today do perform such “obligatory work” in highly controlled environments Factory automation (car assembly) But that is not what robotics research about; the trends and the future look much more interesting Artificial Intelligence

Classical activity decomposition Locomotion (moving around, going places) factory delivery, Mars Pathfinder, lawnmowers, vacuum cleaners... Manipulation (handling objects) factory automation, automated surgery... This divides robotics into two basic areas mobile robotics manipulator robotics … but these are merging in domains like robot pets, robot soccer, and humanoids Artificial Intelligence

An assortment of robots… Artificial Intelligence

Anthropomorphic Robots Artificial Intelligence

Artificial Intelligence Animal-like Robots Artificial Intelligence

Artificial Intelligence Humanoid Robots Asimo (Honda) QRIO Artificial Intelligence DB (ATR) Robonaut (NASA) Sony Dream Robot

Artificial Intelligence Outline Introduction Robotics: what it is, what it isn’t, and where it came from Key concepts Brief history Robot control architectures Deliberative control Reactive control Hybrid control Behavior-based control Artificial Intelligence

A Brief History of Robotics Robotics grew out of the fields of control theory, cybernetics and AI Robotics, in the modern sense, can be considered to have started around the time of cybernetics (1940s) Early AI had a strong impact on how it evolved (1950s-1970s), emphasizing reasoning and abstraction, removal from direct situatedness and embodiment In the 1980s a new set of methods was introduced and robots were put back into the physical world Artificial Intelligence

Artificial Intelligence Cybernetics Pioneered by Norbert Wiener in the 1940s Combines principles of control theory, information science and biology Sought principles common to animals and machines, especially with regards to control and communication Studied the coupling between an organism and its environment Artificial Intelligence

W. Grey Walter’s Tortoise Machina Speculatrix” (1953) 1 photocell, 1 bump sensor, 1 motor, 3 wheels, 1 battery, analog circuits Behaviors: seek light head toward moderate light back from bright light turn and push recharge battery Uses reactive control, with behavior prioritization Artificial Intelligence

Artificial Intelligence Braitenberg Vehicles Valentino Braitenberg (1980) Thought experiments Use direct coupling between sensors and motors Simple robots (“vehicles”) produce complex behaviors that appear very animal, life-like Excitatory connection The stronger the sensory input, the stronger the motor output Light sensor  wheel: photophilic robot (loves the light) Inhibitory connection The stronger the sensory input, the weaker the motor output Light sensor  wheel: photophobic robot (afraid of the light) Artificial Intelligence

Artificial Intelligence Example Vehicles Wide range of vehicles can be designed, by changing the connections and their strength Vehicle 1: One motor, one sensor Vehicle 2: Two motors, two sensors Excitatory connections Vehicle 3: Inhibitory connections Vehicle 1 Being “ALIVE” “FEAR” and “AGGRESSION” Vehicle 2 “LOVE” Artificial Intelligence

Artificial Intelligence Officially born in 1956 at Dartmouth University Marvin Minsky, John McCarthy, Herbert Simon Intelligence in machines Internal models of the world Search through possible solutions Plan to solve problems Symbolic representation of information Hierarchical system organization Sequential program execution Artificial Intelligence

Artificial Intelligence AI and Robotics AI influence to robotics: Knowledge and knowledge representation are central to intelligence Perception and action are more central to robotics New solutions developed: behavior-based systems “Planning is just a way of avoiding figuring out what to do next” (Rodney Brooks, 1987) First robots were mostly influenced by AI (deliberative) Artificial Intelligence

Artificial Intelligence Outline Introduction Robotics: what it is, what it isn’t, and where it came from Key concepts Brief history Robot control architectures Deliberative control Reactive control Hybrid control Behavior-based control Artificial Intelligence

Artificial Intelligence Control Architecture A robot control architecture provides the guiding principles for organizing a robot’s control system It allows the designer to produce the desired overall behavior The term architecture is used similarly as “computer architecture” Set of principles for designing computers from a collection of well-understood building blocks The building-blocks in robotics are dependent on the underlying control architecture Artificial Intelligence

Artificial Intelligence Robot Control Robot control is the means by which the sensing and action of a robot are coordinated There are infinitely many ways to program a robot, but there are only few types of robot control: Deliberative control Reactive control Hybrid control Behavior-based control Artificial Intelligence

Spectrum of robot control From “Behavior-Based Robotics” by R. Arkin, MIT Press, 1998 Artificial Intelligence

Artificial Intelligence Thinking vs. Acting Thinking/Deliberating involves planning (looking into the future) to avoid bad solutions flexible for increasing complexity slow, speed decreases with complexity thinking too long may be dangerous requires (a lot of) accurate information Acting/Reaction fast, regardless of complexity innate/built-in or learned (from looking into the past) limited flexibility for increasing complexity Artificial Intelligence

Robot control approaches Reactive Control Don’t think, (re)act. Deliberative (Planner-based) Control Think hard, act later. Hybrid Control Think and act separately & concurrently. Behavior-Based Control (BBC) Think the way you act. Artificial Intelligence

Artificial Intelligence A Brief History Deliberative Control (late 70s) Reactive Control (mid 80s) Subsumption Architecture (Rodney Brooks) Behavior-Based Systems (late 80s) Hybrid Systems (late 80s/early 90s) Artificial Intelligence

Artificial Intelligence Outline Introduction Robotics: what it is, what it isn’t, and where it came from Key concepts Brief history Robot control architectures Deliberative control Reactive control Hybrid control Behavior-based control Artificial Intelligence

Deliberative Control: Think hard, then act! In DC the robot uses all the available sensory information and stored internal knowledge to create a plan of action: sense  plan  act (SPA) paradigm Limitations Planning requires search through potentially all possible plans  these take a long time Requires a world model, which may become outdated Too slow for real-time response Advantages Capable of learning and prediction Finds strategic solutions Artificial Intelligence

Artificial Intelligence Early AI Robots Shakey (1960, Stanford Research Institute) Stanford Cart (1977) and CMU rover (1983) Interpreting the structure of the environment from visual input involved complex processing and required a lot of deliberation Used state-of-the-art computer vision techniques to provide input to a planner and decide what to do next (how to move) Artificial Intelligence

Artificial Intelligence Outline Introduction Robotics: what it is, what it isn’t, and where it came from Key concepts Brief history Robot control architectures Deliberative control Reactive control Hybrid control Behavior-based control Artificial Intelligence

Reactive Control: Don’t think, react! Technique for tightly coupling perception and action to provide fast responses to changing, unstructured environments Collection of stimulus-response rules Limitations No/minimal state No memory No internal representations of the world Unable to plan ahead Advantages Very fast and reactive Powerful method: animals are largely reactive Artificial Intelligence

Vertical v. Horizontal Systems Traditional (SPA): sense – plan – act Subsumption: (Rodney Brooks) “The world is its own best model.” Artificial Intelligence

The Subsumption Architecture Principles of design systems are built incrementally components are task-achieving actions/behaviors (avoid-obstacles, find-doors, visit-rooms) all rules can be executed in parallel, not in a sequence components are organized in layers, from the bottom up lowest layers handle most basic tasks newly added components and layers exploit the existing ones Artificial Intelligence

Artificial Intelligence Subsumption Layers First, we design, implement and debug layer 0 Next, we design layer 1 When layer 1 is designed, layer 0 is taken into consideration and utilized, its existence is subsumed Layer 0 continues to function Continue designing layers, until the desired task is achieved Higher levels can Inhibit outputs of lower levels Suppress inputs of lower levels level 2 level 1 level 0 sensors actuators AFSM inputs outputs suppressor inhibitor I s Artificial Intelligence

Subsumption Architecture Validation Practically demonstrated on navigation, 6-legged walking, chasing, soda-can collection, etc. Artificial Intelligence

Artificial Intelligence Outline Introduction Robotics: what it is, what it isn’t, and where it came from Key concepts Brief history Robot control architectures Deliberative control Reactive control Hybrid control Behavior-based control Artificial Intelligence

Hybrid Control: Think and act independently & concurrently! Combination of reactive and deliberative control Reactive layer (bottom): deals with immediate reaction Deliberative layer (top): creates plans Middle layer: connects the two layers Usually called “three-layer systems” Major challenge: design of the middle layer Reactive and deliberative layers operate on very different time-scales and representations (signals vs. symbols) These layers must operate concurrently Currently one of the two dominant control paradigms in robotics Artificial Intelligence

Reaction – Deliberation Coordination Flakey Selection: Planning is viewed as configuration Advising: Planning is viewed as advice giving Adaptation: Planning is viewed as adaptation Postponing: Planning is viewed as a least commitment process TJ Artificial Intelligence

Artificial Intelligence Outline Introduction Robotics: what it is, what it isn’t, and where it came from Key concepts Brief history Robot control architectures Deliberative control Reactive control Hybrid control Behavior-based control Artificial Intelligence

Behavior-Based Control Think the way you act! An alternative to hybrid control, inspired from biology Behavior-based control involves the use of “behaviors” as modules for control Historically grew out of reactive systems, but not constrained Has the same expressiveness properties as hybrid control The key difference is in the “deliberative” component Artificial Intelligence

Artificial Intelligence What Is a Behavior? Rules of implementation Behaviors achieve or maintain particular goals (homing, wall-following) Behaviors are time-extended processes Behaviors take inputs from sensors and from other behaviors and send outputs to actuators and other behaviors Behaviors are more complex than actions (stop, turn-right vs. follow-target, hide-from-light, find-mate etc.) Artificial Intelligence

Principles of BBC Design Behaviors are executed in parallel, concurrently Ability to react in real-time Networks of behaviors can store state (history), construct world models/representation and look into the future Use representations to generate efficient behavior Behaviors operate on compatible time-scales Ability to use a uniform structure and representation throughout the system Artificial Intelligence

Behavior Coordination Behavior-based systems require consistent coordination between the component behaviors for conflict resolution Coordination of behaviors can be: Competitive: one behavior’s output is selected from multiple candidates Cooperative: blend the output of multiple behaviors Combination of the above two Artificial Intelligence

Competitive Coordination Arbitration: winner-take-all strategy  only one response chosen Behavioral prioritization Subsumption Architecture Action selection/activation spreading (Pattie Maes) Behaviors actively compete with each other Each behavior has an activation level driven by the robot’s goals and sensory information Voting strategies Behaviors cast votes on potential responses Artificial Intelligence

Cooperative Coordination Fusion: concurrently use the output of multiple behaviors Major difficulty in finding a uniform command representation amenable to fusion Fuzzy methods Formal methods Potential fields Motor schemas Dynamical systems Artificial Intelligence

Example of Behavior Coordination Fusion: flocking (formations) Arbitration:  foraging (search, coverage) Artificial Intelligence

Example of representation A network of behaviors representing spatial landmarks, used for path planning by message-passing (Matarić 90) Artificial Intelligence

Behavior-Based Control summary Alternative to hybrid systems; encourages uniform time-scale and representation throughout the system Scalable and robust Behaviors are reusable; behavior libraries Facilitates learning Requires a clever means of distributing representation and any potentially time-extended computation Artificial Intelligence

Artificial Intelligence Robotics Challenges Perception Limited, noisy sensors Actuation Limited capabilities of robot effectors Thinking Time consuming in large state spaces Environments Dynamic, impose fast reaction times Artificial Intelligence

Artificial Intelligence Lessons Learned Move faster, more robustly Think in such a way as to allow this action New types of robot control: Reactive, hybrid, behavior-based Control theory Continues to thrive in numerous applications Cybernetics Biologically inspired robot control AI Non-physical, “disembodied thinking” Artificial Intelligence

Artificial Intelligence Background Readings Ronald Arkin, “Behavior-Based Robotics”, 2001. Artificial Intelligence