CMSC 25000 Artificial Intelligence March 11, 2008 Robotics CMSC 25000 Artificial Intelligence March 11, 2008

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

Mobile Robots

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

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

Real World Complexity Real world is hardest environment Problems: 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

Navigation

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

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

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

Navigation

Navigation

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

Online Search Offline search: Online 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)

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

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

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

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

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

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

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

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

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

Subsumption Problems Relies on raw sensor data Hard to change task 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

Solution Hybrid approach 3 layer architecture 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

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