2. Topics: Introduction to Robotics CS 491/691(X) Lecture 10 Instructor: Monica Nicolescu

3. CS 491/691(X) - Lecture 102 Review The Subsumption Architecture –Herbert, Genghis, The Nerd Herd, Tom and Jerry –Advantages and dissadvantages Behavior-based control –Definitions –Principles of behavior-based control –Toto: a behavior-based mapping robot

4. CS 491/691(X) - Lecture 103 An Example Task: Mapping Design a robot that is capable of: –Moving around safely –Make a map of the environment –Use the map to find the shortest paths to particular places Navigation & mapping are the most common mobile robot tasks

5. CS 491/691(X) - Lecture 104 Map Representation The map is distributed over different behaviors We connect parts of the map that are adjacent in the environment by connecting the behaviors that represent them The network of behaviors represents a network of locations in the environment Topological map: Toto (Matarić ’90)

6. CS 491/691(X) - Lecture 105 Toto’s Behaviors Toto the robot –Ring of 12 sonars, low-resolution compas Lowest level: move around safely, without collisions Next level: following boundaries, a behavior that keeps the robot near walls and other objects Landmark detection: keep track of what was sensed and how it was moving –meandering  cluttered area –constant compass direction, go straight  left, right walls –moving straight, both walls  corridor

7. CS 491/691(X) - Lecture 106 Toto’s Controller

8. CS 491/691(X) - Lecture 107 Building a Map Each landmark was stored in a behavior Whenever a new landmark was discovered a new behavior was added Adjacent landmarks are connected by communication wires This resulted in a topological representation of the environment, i.e., a topological world model

9. CS 491/691(X) - Lecture 108 Toto’s Mapping Behaviors Each landmark was stored in a behavior Each such landmark behavior stored (remembered) some information –landmark type (wall, corridor, irregular) –compass heading –approximate length/size –some odometry my-behavior-type: corridor my-compass-direction: NV my-approximate-location: x,y my-approximate-length: length whenever received (input) if input(behavior-type)= my-behavior-type and input (compass-direction) = my-compass-direction then active <- true

10. CS 491/691(X) - Lecture 109 Localization Whenever a landmark is detected, its description (type, compass direction, etc.) is sent to all behaviors in parallel  the one that matches becomes active When nothing in the map matches  a new place/landmark was discovered and added to the map If an existing behavior was activated, it inhibited any other active behaviors

11. CS 491/691(X) - Lecture 1010 Getting Around Toto can use the map to navigate The behavior that corresponds to the goal sends messages (spreads activation) to all of its neighbors The neighbors send messages to their neighbors in turn So on, until the messages reach the currently active behavior This forms path(s) from the current state to the goal

12. CS 491/691(X) - Lecture 1011 Path Following Toto did not keep a sequence of behaviors Instead, messages were passed continuously –Useful in changing environments How does Toto decide where to go if multiple choices are available? –Rely on the landmark size: behaviors add up their own length as messages are passed from one behavior to another  give path length –Choose the shortest path Thus, one behavior at a time, it reached the goal

13. CS 491/691(X) - Lecture 1012 Toto - Video

14. CS 491/691(X) - Lecture 1013 Expression of Behaviors Example: –Going from a classroom to another What does it involve? –Getting to the destination from the current location –Not bumping into obstacles along the way –Making your way around students on corridors –Deferring to elders –Coping with change in the environment

15. CS 491/691(X) - Lecture 1014 Stimulus-Response Diagrams Behaviors are represented as a general response to a particular stimulus move-to-class avoid-object dodge-student stay-right defer-to-elder COORDINATORCOORDINATOR class location detected object detected student detected path detected elder Actions

16. CS 491/691(X) - Lecture 1015 Finite State Acceptor Diagrams Useful for describing aggregations and sequences of behaviors Finite state acceptor: –Q: set of allowable behavioral states –  : transition function from (input, current state)  new state –q 0 : initial state –F: set of accepting (final) states StartJourney Lost At-class move error reached-class othernot-at-classall

17. CS 491/691(X) - Lecture 1016 Formal Methods Have very useful properties for the programmer –Can be used to verify designer intentions –Facilitate the automatic generation of control systems –Provide a common language for expressing robot behaviors –Provide a framework for formal analysis of the program, adequacy and completeness –Provide support for high-level programming language design

18. CS 491/691(X) - Lecture 1017 Situated Automata Kaelbling & Rosenschein (’91) Controllers are designed as logic circuits Capable of reasoning REX: Lisp-based language –First language to encode a reactive systems with synchronous digital circuitry Gapps: goals are specified more directly –Achievement, maintenance, execution –Logical boolean operators are used to create higher-level goals  generate circuits

19. CS 491/691(X) - Lecture 1018 Situated Automata (defgoalr (ach in-classroom) (if (not start-up) (maint (and (maint move-to-classroom) (maint avoid-objects) (maint dodge-students) (maint stay-to-right-on-path) (maint defer-to-elders) )

20. CS 491/691(X) - Lecture 1019 Discrete Behavioral Encoding The behavior consists of a set finite set of (situation, response) pairs Rule-based systems IF antecedent THEN consequent Condition-action production rules (Nilsson ’94) –Produce durative actions: “move at 5m/s” vs. “move 5 m” The Subsumption Architecture ( whenever condition & consequent)

21. CS 491/691(X) - Lecture 1020 Continuous Behavioral Encoding Continuous response provides a robot an infinite space of potential reactions to the world A mathematical function transforms the sensory input into a behavioral reaction Potential fields –Law of universal gravitation: potential force drops off with the square of the distance between objects –Goals are attractors and obstacles are repulsors –Separate fields are used for each object –Fields are combined (superposition)  unique global field

22. CS 491/691(X) - Lecture 1021 Potential Fields Ballistic goal attraction field Superposition of two fields

23. CS 491/691(X) - Lecture 1022 Potential Fields Advantages –Provide an infinite set of possibilities of reaction –Highly parallelizable Disadvantages –Local minima, cyclic-oscillatory behavior –Apparently, large amount of time required to compute the entire field: reaction is computed only at the robot’s position!

24. CS 491/691(X) - Lecture 1023 Motor Schemas Motor schemas are a type of behavior encoding – Based on neuroscience and cognitive science They are based on schema theory (Arbib) –Explains motor behavior in terms of the concurrent control of many different activities –Schemas store how to react and the way the reaction can be realized: basic units of activity –Schema theory provides a formal language for connecting action and perception –Activation levels are associated with schemas, and determine their applicability for acting

25. CS 491/691(X) - Lecture 1024 Visually Guided Behaviors Michael Arbib & colleagues constructed computer models of visually guided behaviors in frogs and toads Toads & frogs respond visually to –Small moving objects  feeding behavior –Large moving objects  fleeing behavior Behaviors implemented as a vector field (schemas) –Attractive force (vector) along the direction of the fly What happens when presented with two files simultaneously? –The frog sums up the two vectors and snaps between the two files, missing both of them

26. CS 491/691(X) - Lecture 1025 Motor Schemas Provide large grain modularity Schemas act concurrently, in a cooperative but competing manner Schemas are primitives from which more complex behaviors ( assemblages can be constructed) Represented as vector fields

27. CS 491/691(X) - Lecture 1026 Examples of Schemas Obstacle avoid and stay on corridor schemas

28. CS 491/691(X) - Lecture 1027 Schema Representation Responses represented in uniform vector format Combination through cooperative coordination via vector summation No predefined schema hierarchy Arbitration is not used –each behavior has its contribution to the robot’s overall response –gain values control behavioral strengths Here is how:

29. CS 491/691(X) - Lecture 1028 The Role of Gains in Schemas Low gain High gain

30. CS 491/691(X) - Lecture 1029 Designing with Schemas Characterize motor behaviors needed Decompose to most primitive level, use biological guidelines where appropriate Develop formulas to express reaction Conduct simple simulations Determine perceptual needs to satisfy motor schema inputs Design specific perceptual algorithms Integrate/test/evaluate/iterate

31. CS 491/691(X) - Lecture 1030 Foraging Example

32. CS 491/691(X) - Lecture 1031 Strengths and Weaknesses Strengths: – support for parallelism – run-time flexibility – timeliness for development – support for modularity Weaknesses: –hardware retargetability –combination pitfalls (local minima, oscillations)

33. CS 491/691(X) - Lecture 1032 Schema-Based Robots At Georgia Tech (Ron Arkin) Exploration Hall following Wall following Impatient waiting Navigation Docking Escape Forage

34. CS 491/691(X) - Lecture 1033 The DAMN Architecture Distributed Architecture for Mobile Navigation (Rosenblatt 1995) Multi-valued behaviors (at all levels) propose multiple action preferences Each behavior votes for or against sets of actions Arbiter selects max weighted vote sum Practically demonstrated on real-world long-distance navigation Disadvantage: highly heuristic

35. CS 491/691(X) - Lecture 1034 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

36. CS 491/691(X) - Lecture 1035 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 (DAMN) –Behaviors cast votes on potential responses

37. CS 491/691(X) - Lecture 1036 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

38. CS 491/691(X) - Lecture 1037 Emergent Behavior The resulting robot behavior may sometimes be surprising or unexpected  emergent behavior Emergence arises from –A robot’s interaction with the environment –The interaction of behaviors

39. CS 491/691(X) - Lecture 1038 Wall Following A simple wall following controller: –If too close on the left, turn right –If too close on the right, turn left –Otherwise, keep straight If the robot is placed close to a wall it will follow Is this emergent? –The robot has no explicit representations of walls –The controller does not specify anything explicit about following

40. CS 491/691(X) - Lecture 1039 Emergence A “holistic” property, where the behavior of the robot is greater than the sum of its parts A property of a collection of interacting components Often occurs in reactive and behavior-based systems (BBS) Typically exploited in reactive and BBS design

41. CS 491/691(X) - Lecture 1040 Flocking How would you design a flocking behavior for a group of robots? Each robot can be programmed with the same behaviors: –Don’t get too close to other robots –Don’t get too far from other robots –Keep moving if you can When run in parallel these rules will result in the group of robots flocking

42. CS 491/691(X) - Lecture 1041 Readings M. Matarić: Chapters 17, 18