Designing a Reactive Implementation List the steps involved in designing a reactive behavioral system. Use schema theory to program behaviors using object-oriented programming principles. Design a complete behavioral system, including coordinating and controlling multiple concurrent behaviors. Describe the two methods for assembling primitive behaviors into abstract behaviors: finite state machines and scripts. Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary Pop quiz: fill in a behavior table for phototropism (Lab #4) Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Behaviors as objects in OOP Schema is a class with optional coordinated control program Perceptual schema, motor schema and behavior are all subclasses of schema (class diagram p. 157) Behavior has (class diagram p. 157) One or more perceptual schemas One or more motor schemas One or more behaviors Primitive behavior has only one perceptual schema, one motor schema, no coordinated control program; often prgrammed as a single method Behaviors that are assembled from other behaviors or have multiple perceptual and motor schemas are abstract behaviors (not the same as abstract class in OOP) Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Example: primitive move-to-goal behavior Robot placed in empty office with red coke cans and white cups placed randomly, blue recycling bins in two corners, different colored trash bin in 2 corners. Goal: pick up the most trans and place in correct bin; red and blue are more easily perceived, so typical strategy was t recycle coke cans first Basic behavior: move_to_goal(goal_color) Note: PFields is a class with 5 primitive potential fields, serves as a library; percept is local to behavior Object Behavioral analog identifier Data Percept Goal_angle Goal_strength Methods Perceptual_schema Motor_schama Extract_goal(goal_color) Pfields.attraction(goal_angle, goal_strength) Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Example: abstract follow-corridor behavior Class diagrams for 2 different approaches shown on p. 161 Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation Releasers in OOP Releaser is a perceptual schema not bound to a motor schema Two approaches: Look for red with extract_color, signal main program when red is seen, main instantiates move_to_goal(red) Let behavior always be active, but if red is not seen, behavior returns identity function (0,0,0) for potential field Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Steps in Designing a Reactive Behavioral System -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary Have them note the similarities to the Waterfall Life Cycle in software engineering Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation CSM 1994 UGV Competition (this is from 1996, but it’s a way better picture) Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary Fully autonomous vehicle, all on-board, navigate a course 10ft wide, about 800ft long Obstacles Carry a 20 pound payload Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

1-3. Describe the Task, Robot, Environment Camcorder on a Panning mast, going to A framegrabber 33MHz 486 PC running Lynx (commercial unix) Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary Sonar on a panning mast Note the hardware limitations: b/w framegrabber and ridiculously slow processor by today’s standards. Good time to discuss Moore’s Law. Have to use computer vision… black &white, slow processor speeds (didn’t get to design the robot) White (bright) should be in the center of the image Reflections on grass are white, but random so average out If stay in middle, never encounter an obstacle! 150ms update rate needed for steering to stay in control at ~1.5mph Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

4. Describe how robot should act Follow the line and stay in the middle Follow-line Only need the Camcorder! Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

5-6 Refine and test each behavior Follow-line Worked with toilet paper (indoors), Worked with athletic line tape (outdoor) Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

7 Test with other behaviors “Full dress rehearsal” Oops, bales of hay are bright compared to grass, change the centroid to cause collision Go back to step 4: Follow line until “see” an obstacle, then just go straight until things return to normal Hard to do visually in real time Sonar! Look to the side and when something is close, it’s a bale, so go straight Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation Final System Round 1 OOPS: sonar connection off so it hit the bale Round 2 White shoes and dandelions, plus Killer Bale Demo round Hill vs. implicit flat earth assumption Round 3 Trapped by sand, but $5K richer! Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation Main Points Let the WORLD BE ITS OWN BEST REPRESENTATION and CONTROL STRUCTURE “line” wasn’t a “line” just centroid of brightest pixels in the image Pick up trash: if can is in gripper, then go to the recycle bin Design process was iterative; rarely get a workable emergent behavior on the first try There is no single right answer Could have been done with subsumption, pfields, rules, whatever Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation Other Lessons Learned Don’t count on getting much work done in the field! Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary “A river runs through it…” Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Case Study and Field Trip: Polar Robotics Existing robots DANTE, DANTE II Nomad Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation DANTE Task explore volcanoes in Antarctica (reduce risk to vulcanologists) Environment steep, rocky slopes Robot legged “framewalker” Behaviors rappel down Refine and test Test with other behaviors 1997 South America, failed 1998 Antarctica, med. success Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation Lessons to Be Learned A robot is part of a system, must design system example tether breaking Testing and evaluation really does take ~30% of the time allocated (just like software engineering) and more to fix the problem NASA technical readiness levels Level 1: just an idea Level 6: demonstrated on a realistic test bed Level 9: has flown, fully operational Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation Nomad Task search for and analyze metorities Environment Antarctica Robot hybrid mobility, LADAR (laser radar), omnivision, sample sensors Behaviors search an area using GPS, avoid negative/positive obstacles, stop at possible metorities and alert user. Can be controlled by human Refine and Test Test with other behaviors 1999 2000 success Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation Nomad Omnivision Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Hyperion (really about Mars) Task navigate Arctic Environment Arctic Robot wheeled, uses solar Behaviors Refine and test Test with other behaviors 2002 success Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

NASA HMP / Mars Society Flashline Project Robotics Support Confined space inspection of Mars habitat Marsupial deployment for fault identification Human robot team for collaborative exploration of hazardous areas & narrow fissures in search of micro-organisms Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Case Study in Pfields: Docking Needed for marsupial robots Note use of reactivity simple perception pfields improvement over human Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Daughter-centric Docking Behavior Potential field methodology: orientation, Xbar= tangent vector overall size= attraction vector (Arkin, Murphy 90) Silver Bullet: AMD K-6 400MHz clearance=+/-2cm +/-5 degrees Bujold: Inuktun mVGTV Pan only, color camera, optics at about 2ms, skid steering, no on-board processor Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Docking Behavior Results 48 data points (3 per 16 stations) on time to completion by angle by radial position Larger angles, the slower, and farther away the slower 2 failures: 3, 7 Fastest: 26 sec. Slowest: 81 sec. Pfields weren’t intended to work behind the gate Optics dictated 120 deg, 2m Approach Zone Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Docking Behavior Results: teleoperation 17% faster Performance comparison: - 66 data points, 22 teleoperators - paired sample t-test (conf>95%) Communications data: -36 data points, 6 teleoperators Teleoperators did better only at 1,3 where the robot wasn’t supposed to be able to dock from. Noticed that teleoperators used a different strategy; opportunity for improvement Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation Docking Vision Spherical Coordinate Transform space instead of HSV, RGB Biomimetic vision: gradient, looming O(m x n) lighting insensitive color segmentation 8-10 frames/sec on commercial hardware vs. 30 frames/sec for Cognachrome hardware Robust algorithm 2-colored landmark to prevent confusion imprinting and time of day look up tables (LUT) if time-out, mother moves a color is a circle on the plane Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Light insensitive color segmentation LUT (time of day, weather) >15 tries Compute region statistics Perform connected components Initial values Extract regions Imprint Adapt new values for region Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Indoor Data Collection and Demonstrations Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Outdoor Demonstrations Imprint at dawn (light mist), return 6 hours later As expected, mediocre performance from dusk to night (~50%) due to headlight reflection Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Additional Case Studies: Commercial Products Robomow cutting grass in residential areas My Real Baby toy dolls Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation Robomow Behaviors? Random Avoid Avoid(bump=obstacle) Avoid(wire=boundary) Stop Stop(tilt=ON) All active www.friendlymachines.com Overview History Reactive USAR Summary Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation My Real Baby Behaviors? Touch-> Awake Upside down & Awake-> Cry Awake & Hungry -> Cry Awake & Lonely -> Cry Note can get crying from multiple behaviors Note internal state (countdown timer on Lonely) www.irobot.com Overview History Reactive USAR Summary Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Commercial Case Studies Purely reactive May not need subsumption or pfields, very straightforward State of practice is much less complicated than state of art! Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Design Tool: Behavior Table An agent is attracted to light. If it sees light, it heads in that direction. If it encounters an obstacle, it turns either left or right, favoring the direction of the light. If there is no light, it sits and waits. But if an obstacle appears, the agent runs away. Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary 1. photropism 2. Obstacle avoidance This is using the lab #4 on subsumption behaviors. Note that atLight is really part of move2light, since it’s the STOPPING CASE for move2light Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation Behavior Table An agent is attracted to light. If it sees light, it heads in that direction. If it encounters an obstacle, it turns either left or right, favoring the direction of the light. If there is no light, it sits and waits. But if an obstacle appears, the agent runs away. Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary Releaser Behavior Motor Schema Percept Perceptual Schema Light phototropism move2Light():Attraction Light: direction & strength Brightest(dir), atLight() Range <tasked level> Obstacle avoidance avoid(): turn left or right; runaway() proximity Obstacle() Notice that the perceptual schemas were hidden in the previous drawing. Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation Robomow Releaser Behavior Motor Schema Percept Perceptual Schema Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary Notice that the perceptual schemas were hidden in the previous drawing. Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation My Real Baby Releaser Behavior Motor Schema Percept Perceptual Schema Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary Notice that the perceptual schemas were hidden in the previous drawing. Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Assemblages of Behaviors What to do if have a SEQUENCE of behaviors as well as CONCURRENCY? Two equivalent methods Finite state automata (FSA) Scripts (other ways too) Two approaches: Stuff the coordination mechanism into a separate controlling program (like a main()) Hurts portability, adding new behaviors “on top” Stuff the coordination mechanism into a meta-behavior or abstract behavior using recursive-ness of schemas Coordination mechanism is the coordinated control program part of the behavioral schema Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary They should have generated a scenario for what they want the robot to do Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Example of a Sequence: Catch Suzy! Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation Example Continued “Tracking Phase” move to goal avoid obstacle “Pick up Phase” fine position pick up “Return Home Phase” same behaviors, just DIFFERENT INSTANTIATION OF GOAL Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation FSA: M={K,S,d,s,F} Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary K: all the states, each is “q”- behaviors d: transition function, d(q,s)= new behavior S: inputs that agent can “see”, each is s– stimulus/affordances q0: Start state(s)- part of K F: Terminating state(s)- part of K have them fill in the table: q, small sigma, delta Behavior table p. 175 Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Class Exercise: Catch Suzy Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation EX: Pick Up Trash Behavior table p. 179 FSA p. 181 Abstract behavior using subsumption p. 183 Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation FSA Summary “State” doesn’t mean “state of world” (bad); it means more “where the innate releasing mechanism is currently at” (good) Advantages Formal mechanism Have to fill in the table, so a way of spotting unforeseen effects Disadvantages Hard to keep up with implicit behaviors Ex. Avoid obstacle is often running throughout ALL states, gets tedious to express it formally Tend to add explicit variable for transitions, rather than rely on emergent behavior from environment Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation Scripts UGV competition really didn’t have a sequence of behaviors, just toggling back and forth Scripts are equivalent to FSA but may be more natural or readable for sequences Think of robot and task in terms of a screenplay Resulting script for an abstract behavior is usually the same as the programming logic derived from FSA Ex: pick up trash, p. 186-187 Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation Scripts Script Behavior Analog Examples Goal Task Find victims in rubble Places Environment, applicability or “taskability” for new tasks Collapsed buildings Actors Behaviors explore(), dance(), avoid(), crawl, move2void, move2victim Props, cues Percepts Voids: Dark, concave Victims: heat, motion, color Causal Chain Sequence of behavior Explore, dance, move2void, crawl, move2victim, dropRadio Subscripts Exception handling If lose communications, return home Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation Scripts Summary Will see more of it in Ch10: Topological Navigation Advantages A more storyboard like way of thinking about the behaviors If-then, switch style of programming like FSA Since a Script is “in” a behavior, other behaviors such as avoid can be running concurrently without having to appear “in” the script Exception handling is a big plus in Real Life Disadvantages Can be a bit of overkill for simple sequences, especially with C++ Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation

Chapter 5: Designing a Reactive Implementation Summary Design of reactive systems is identical to design of object-oriented software systems Highly modular, can test behaviors independently Follows the basic steps in the Waterfall Lifecycle Describe task, robot, environment, how robot should act, refine behaviors, test independently, test together. Except lots more iteration, making it more like Spiral Behavior tables can help keep track of what’s a behavior and its releasers and percepts Sequences of behaviors, rather than combinations, can be controlled by a separate routine or by adding a routine to the coordinated control program in the behavioral schema FSA and Scripts are two main methods Design -Beh. Table -Steps Case Study Coordination -FSA -Scripts Summary Introduction to AI Robotics (MIT Press) Chapter 5: Designing a Reactive Implementation