1. Learning minimal representations for visual navigation Dr. William H. Warren Dept. of Cognitive & Linguistic Sciences Dr. Leslie Kaelbling Dept. of Computer Science Brown University NSF Learning & Intelligent Systems Principal Investigator’s Meeting Washington, DC May 3-4, 1999

2. Personnel PI:Dr. William H. Warren, CLS Co-PI’s: Dr. Leslie Kaelbling, CS Dr. Michael Tarr, CLS Post-Doc: Dr. Andrew Duchon*, CLS Grads (3): Vlada Aginsky*, Psych Melissa Bud*, CLS Sam Heath*, CS Undergrads (6): Kevin Sikorsky, CS Phil Levis, CS Carl Hill-Popper* Theo van der Zee*, CS Brent Shields*, CLS Stephanie Sahuc*, Engineering * supported by NSF funds

3. Motivation Robot at Home Depot vs. Mars  specified vs. learned layout  biological solutions inform robotics  use robot platform to test biological hypotheses Working ideas  clever solutions based on minimal knowledge  build upon basic perceptual-motor behaviors  take advantage of task constraints Questions 1. Nature of environmental knowledge? –Geometry, landmarks 2. Dependence on task during learning? 3. Strategies for active navigation? –updating position & orientation –map, route, landmark, view-based navigation

4. Activities (Year 1) Field trip to corn maze Lab meetings & boot camp Design experiments Program displays Laboratory creation...

5. Virtual Environment Navigation Lab Intersense sonic/inertial tracking system  40 x 40 ft tracking area Kaiser PV-80 head-mounted display  65˚ H X 50˚ V  VGA graphics Silicon Graphics Onyx2 workstation  Sense8 WorldToolKit software Jack Loomis on the HolodeckWhat Jack sees: The Funhouse

6. Human Experiments In Progress Study 1: Path integration  How update position & orientation?  Triangle completion task  Manipulate available information: –Optic flow –Vestibular/proprioceptive –Efference –Landmark properties Study 2: Learning novel environments  Learning phase –follow route, self-directed search, random exploration  Transfer to distorted environment –1D stretch; shear –change landmark properties  Test phase –find goal, take shortcuts

7. Visual Robot Navigation Low-level behaviors are built in or tuned with reinforcement learning  Avoid obstacles using optical flow  Fixate and drive to a distant object Map starts as a graph of views with behaviors on the arcs With further experience  Aggregate different views of the same place  Incorporate metric information

8. Robot Experimental Set-Up Robot works in same virtual environment as human subjects No need for the head-mounted display!  Mainline visual imagery into robot vision system Easy to simulate robot motion Most development work in software-only environment Validation runs on real robot wearing head tracker

9. Current Progress Obstacle avoidance & wall-following using optical flow from textured objects in virtual scene Simple histogram-based view representation and matching Learn a route in the environment  Human drives robot through corridors with joystick  Robot finds sequence of its behaviors that are most consistent with specified route  Stores route as views of choice points connected by behaviors  Can re-create the route

10. Technical Issues Representation and matching of views  Pixel arrays vs. histograms  2D vs. 3D Aggregation of views into places  Role of odometry  Role of 3D geometry Role of metric information  Angles and distances on arcs vs. real 2D embedding