Introduction to mobile robots -2: Introduction to mobile robots -2 Slides modified from Maja Mataric’s CSCI445, USC


Last time we saw:: Last time we saw: Defining “robot” What makes a robot Sensors, sensor space State, state space Action/behavior, effectors, action space The spectrum of control Reactive systems


Lecture Outline: Lecture Outline More on the spectrum of control Deliberative and hybrid control A brief history of robotics Feedback control Cybernetics Artificial Intelligence (AI) Early robotics Robotics today Why is robotics hard?


Control: Control Robot control refers to the way in which the sensing and action of a robot are coordinated. The many different ways in which robots can be controlled all fall along a well-defined spectrum of control.


Control Approaches: Control Approaches Reactive Control Don’t think, (re)act. Deliberative Control Think hard, act later. Hybrid Control Think and act independently, in parallel. Behavior-Based Control Think the way you act.


Reactive Systems: Reactive Systems Collections of sense-act (stimulus-response) rules Inherently concurrent (parallel) No/minimal state No memory Very fast and reactive Unable to plan ahead Unable to learn


Deliberative Systems: Deliberative Systems Based on the sense->plan->act (SPA) model Inherently sequential Planning requires search, which is slow Search requires a world model World models become outdated Search and planning takes too long


Hybrid Systems: Hybrid Systems Combine the two extremes reactive system on the bottom deliberative system on the top connected by some intermediate layer Often called 3-layer systems Layers must operate concurrently Different representations and time-scales between the layers The best or worst of both worlds?


Behavior-Based Systems: Behavior-Based Systems An alternative to hybrid systems Have the same capabilities the ability to act reactively the ability to act deliberatively There is no intermediate layer A unified, consistent representation is used in the whole system=> concurrent behaviors That resolves issues of time-scale


A Brief History: A Brief History Feedback control Cybernetics Artificial Intelligence Early Robotics


Feedback Control: Feedback Control Feedback: continuous monitoring of the sensors and reacting to their changes. Feedback control = self-regulation Two kinds of feedback: Positive Negative The basis of control theory


- and + Feedback: - and + Feedback Negative feedback acts to regulate the state/output of the system e.g., if too high, turn down, if too low, turn up thermostats, toilets, bodies, robots... Positive feedback acts to amplify the state/output of the system e.g., the more there is, the more is added lynch mobs, stock market, ant trails...


Uses of Feedback: Uses of Feedback Invention of feedback as the first simple robotics (does it work with our definition)? The first example came from ancient Greek water systems (toilets) Forgotten and re-invented in the Renaissance for ovens/furnaces Really made a splash in Watt's steam engine


Cybernetics: Cybernetics Pioneered by Norbert Wiener (1940s) (From Greek “steersman” of steam engine) Marriage of control theory (feedback control), information science and biology Seeks principles common to animals and machines, especially for control and communication Coupling an organism and its environment (situatedness)


W. Grey Walter’s Tortoise: W. Grey Walter’s Tortoise Machina Speculatrix 1 photocell & 1 bump sensor, 1 motor Behaviors: seek light head to weak light back from bright light turn and push recharge battery Reactive control


Turtle Principles: Turtle Principles Parsimony: simple is better (e.g., clever recharging strategy) Exploration/speculation: keeps moving (except when charging) Attraction (positive tropism): motivation to approach light Aversion (negative tropism): motivation to avoid obstacles, slopes Discernment: ability to distinguish and make choices, i.e., to adapt


The Walter Turtle in Action: The Walter Turtle in Action


Braitenberg Vehicles: Braitenberg Vehicles Valentino Braitenberg (early 1980s) Extended Walter’s model in a series of thought experiments Also based on analog circuits Direct connections (excitatory or inhibitory) between light sensors and motors Complex behaviors from simple very mechanisms


Braitenberg Vehicles: Braitenberg Vehicles Examples of Vehicles: V1: V2: http://people.cs.uchicago.edu/~wiseman/vehicles/


Braitenberg Vehicles: Braitenberg Vehicles By varying the connections and their strengths, numerous behaviors result, e.g.: “fear/cowardice” - flees light “aggression” - charges into light “love” - following/hugging many others, up to memory and learning! Reactive control Later implemented on real robots


Early Artificial Intelligence: Early Artificial Intelligence “Born” in 1955 at Dartmouth “Intelligent machine” would use internal models to search for solutions and then try them out (M. Minsky) => deliberative model! Planning became the tradition Explicit symbolic representations Hierarchical system organization Sequential execution


Artificial Intelligence (AI): Artificial Intelligence (AI) Early AI had a strong impact on early robotics Focused on knowledge, internal models, and reasoning/planning Eventually (1980s) robotics developed more appropriate approaches => behavior-based and hybrid control AI itself has also evolved... But before that, early robots used deliberative control


Early Robots: SHAKEY: Early Robots: SHAKEY At Stanford Research Institute (late 1960s) Vision and contact sensors STRIPS planner Visual navigation in a special world Deliberative


Early Robots: HILARE: Early Robots: HILARE LAAS in Toulouse, France (late 1970s) Video, ultrasound, laser range-finder Still in use! Multi-level spatial representations Deliberative -> Hybrid Control


Early Robots: CART/Rover: Early Robots: CART/Rover Hans Moravec Stanford Cart (1977) followed by CMU rover (1983) Sonar and vision Deliberative control


Robotics Today: Robotics Today Assembly and manufacturing (most numbers of robots, least autonomous) Materials handling Gophers (hospitals, security guards) Hazardous environments (Chernobyl) Remote environments (Pathfinder) Surgery (brain, hips) Tele-presence and virtual reality Entertainment


Why is Robotics hard?: Why is Robotics hard? Sensors are limited and crude Effectors are limited and crude State (internal and external, but mostly external) is partially-observable Environment is dynamic (changing over time) Environment is full of potentially-useful information


Key Issues: Key Issues Grounding in reality: not just planning in an abstract world Situatedness (ecological dynamics): tight connection with the environment Embodiment: having a body Emergent behavior: interaction with the environment Scalability: increasing task and environment complexity