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Robotics
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  1. Robotics CMSC 25000 Artificial Intelligence March 6, 2007

  2. Roadmap • Robotics is AI-complete • Integration of many AI techniques • Classic AI • Search in configuration space • (Ultra) Modern AI • Subsumption architecture • Multi-level control • Conclusion

  3. Mobile Robots

  4. Robotics is AI-complete • Robotics integrates many AI tasks • Perception • Vision, sound, haptics • Reasoning • Search, route planning, action planning • Learning • Recognition of objects/locations • Exploration

  5. Sensors and Effectors • Robotics interact with real world • Need direct sensing for • Distance to objects – range finding/sonar/GPS • Recognize objects – vision • Self-sensing – proprioception: pose/position • Need effectors to • Move self in world: locomotion: wheels, legs • Move other things in world: manipulators • Joints, arms: Complex many degrees of freedom

  6. Real World Complexity • Real world is hardest environment • Partially observable, multiagent, stochastic • Problems: • Localization and mapping • Where things are • What routes are possible • Where robot is • Sensors may be noisy; Effectors are imperfect • Don’t necessarily go where intend • Solved in probabilistic framework

  7. Navigation

  8. Application: Configuration Space • Problem: Robot navigation • Move robot between two objects without changing orientation • Possible? • Complex search space: boundary tests, etc

  9. Configuration Space • Basic problem: infinite states! Convert to finite state space. • Cell decomposition: • divide up space into simple cells, each of which can be traversed “easily" (e.g., convex) • Skeletonization: • Identify finite number of easily connected points/lines that form a graph such that any two points are connected by a path on the graph

  10. Skeletonization Example • First step: Problem transformation • Model robot as point • Model obstacles by combining their perimeter + path of robot around it • “Configuration Space”: simpler search

  11. Navigation

  12. Navigation

  13. Navigation as Simple Search • Replace funny robot shape in field of funny shaped obstacles with • Point robot in field of configuration shapes • All movement is: • Start to vertex, vertex to vertex, or vertex to goal • Search: Start, vertices, goal, & connections • A* search yields efficient least cost path

  14. Online Search • Offline search: • Think a lot, then act once • Online search: • Think a little, act, look, think,.. • Necessary for exploration, (semi)dynamic env • Components: Actions, step-cost, goal test • Compare cost to optimal if env known • Competitive ratio (possibly infinite)

  15. Online Search Agents • Exploration: • Perform action in state -> record result • Search locally • Why? DFS? BFS? • Backtracking requires reversibility • Strategy: Hill-climb • Use memory: if stuck, try apparent best neighbor • Unexplored state: assume closest • Encourages exploration

  16. Acting without Modeling • Goal: Move through terrain • Problem I: Don’t know what terrain is like • No model! • E.g. rover on Mars • Problem II: Motion planning is complex • Too hard to model • Solution: Reactive control

  17. Reactive Control Example • Hexapod robot in rough terrain • Sensors inadequate for full path planning • 2 DOF*6 legs: kinematics, plan intractable

  18. Model-free Direct Control • No environmental model • Control law: • Each leg cycles: on ground; in air • Coordinate so that 3 legs on ground (opposing) • Retain balance • Simple, works on flat terrain

  19. Handling Rugged Terrain • Problem: Obstacles • Block leg’s forward motion • Solution: Add control rule • If blocked, lift higher and repeat • Implementable as FSM • Reflex agent with state

  20. FSM Reflex Controller Retract, lift higher yes no S3 Stuck? S4 Move Forward Set Down Lift up S2 S1 Push back

  21. Emergent Behavior • Reactive controller walks robustly • Model-free; no search/planning • Depends on feedback from the environment • Behavior emerges from interaction • Simple software + complex environment • Controller can be learned • Reinforcement learning

  22. Subsumption Architecture • Assembles reactive controllers from FSMs • Test and condition on sensor variables • Arcs tagged with messages; sent when traversed • Messages go to effectors or other FSMs • Clocks control time to traverse arc- AFSM • E.g. previous example • Reacts to contingencies between robot and env • Synchronize, merge outputs from AFSMs

  23. Subsumption Architecture • Composing controllers from composition of AFSM • Bottom up design • Single to multiple legs, to obstacle avoidance • Avoids complexity and brittleness • No need to model drift, sensor error, effector error • No need to model full motion

  24. Subsumption Problems • Relies on raw sensor data • Sensitive to failure, limited integration • Typically restricted to local tasks • Hard to change task • Emergent behavior – not specified plan • Hard to understand • Interactions of multiple AFSMs complex

  25. Solution • Hybrid approach • Integrates classic and modern AI • 3 layer architecture • Base reactive layer: low-level control • Fast sensor action loop • Executive (glue) layer • Sequence actions for reactive layer • Deliberate layer • Generates global solutions to complex tasks with planning • Model based: pre-coded and/or learned • Slower • Some variant appears in most modern robots

  26. Conclusion • Robotics as AI microcosm • Back to PEAS model • Performance measure, environment, actuators, sensors • Robots as agents act in full complex real world • Tasks, rely on actuators and sensing of environment • Exploits perceptions, learning, and reasoning • Integrates classic AI search, representation with modern learning, robustness, real-world focus