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Berkeley COMPSCI 188 - Advanced Applications: Robotics / Vision / Language

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1CS 188: Artificial IntelligenceFall 2011Advanced Applications:Robotics / Vision / LanguageDan Klein – UC BerkeleyMany slides from Sebastian Thrun, Pieter Abbeel, Jitendra Malik1Announcements This week: No sections this week Office hours modified (moved earlier), see Piazza Final contest cutoff is 8pm Wednesday Grades: W1-3, P1-4, Midterm in glookup, please check P5: full credit in grade computations22So Far: Foundational Methods3Now: Advanced Applications43Autonomous VehiclesAutonomous vehicle slides adapted from Sebastian Thrun[DEMO: Race, Short] 150 mile off-road robot race across the Mojave desert Natural and manmade hazards No driver, no remote control No dynamic passing 150 mile off-road robot race across the Mojave desert Natural and manmade hazards No driver, no remote control No dynamic passingGrand Challenge: Barstow, CA, to Primm, NV[DEMO: GC Bad, Good]4An Autonomous Car5 LasersCameraRadarE-stopGPSGPS compass6 ComputersIMUSteering motorControl ScreenActions: Steering ControlErrorSteering Angle(with respect to trajectory)5Sensors: Laser Readings[DEMO: LIDAR]123Readings: No Obstacles6∆ZReadings: ObstaclesRaw Measurements: 12.6% false positivesObstacle DetectionTrigger if |Zi−Zj| > 15cm for nearby zi, zj7xt+2xtxt+1zt+2ztzt+1Probabilistic Error ModelGPSIMUGPSIMUGPSIMUHMM Inference: 0.02% false positivesRaw Measurements: 12.6% false positivesHMMs for Detection8Environmental Tracking[DEMO: PEOPLE]Sensors: Camera9Object RecognitionQueryTemplateVision slides adapted from Jitendra MalikShape ContextCount the number of points inside each bin, e.g.:Count = 4Count = 10... Compact representation of distribution of points relative to each point1810Shape Context19Similar RegionsColor indicates similarity using local descriptors2011Match for Image Similarity21Vision for a Car[DEMO: LIDAR 1]12Self-Supervised Vision[DEMO: LIDAR 2]Complex Robot Control[demo – quad initial]13Robotic Control Tasks Perception / Tracking Where exactly am I? What’s around me? Low-Level Control How to move from position A to position B Safety vs efficiency High-Level Control What are my goals? What are the optimal high-level actions?Low-Level Planning Low-level: move from configuration A to configuration B14A Simple Robot Arm Configuration Space What are the natural coordinates for specifying the robot’s configuration? These are the configuration space coordinates Can’t necessarily control all degrees of freedom directly Work Space What are the natural coordinates for specifying the effector tip’s position? These are the work spacecoordinatesCoordinate Systems Workspace: The world’s (x, y) system Obstacles specified here Configuration space The robot’s state Planning happens here Obstacles can be projected to here15Obstacles in C-Space What / where are the obstacles? Remaining space is free spaceExample: A Less Simple Arm[DEMO]16Probabilistic Roadmaps Idea: sample random points as nodes in a visibility graph This gives probabilistic roadmaps Very successful in practice Lets you add points where you need them If insufficient points, incomplete or weird paths Demonstrate path across the “training terrain” Run apprenticeship learning to find a set of weights w Receive “testing terrain” (a height map)  Find a policy for crossing the testing terrain.High-Level Control17High DOF Robots[DEMOS]Videos from Pieter Abbeel, Jean-Claude


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Berkeley COMPSCI 188 - Advanced Applications: Robotics / Vision / Language

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