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Cognitive Robotics: Lessons from the SmartWheeler project

Cognitive Robotics: Lessons from the SmartWheeler project. Joelle Pineau, jpineau@cs.mcgill.ca School of Computer Science, McGill University September 22, 2010. Cognitive robotics.

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Cognitive Robotics: Lessons from the SmartWheeler project

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  1. Cognitive Robotics: Lessons from the SmartWheeler project Joelle Pineau, jpineau@cs.mcgill.ca School of Computer Science, McGill University September 22, 2010

  2. Cognitive robotics • Main scientific goal: Design robots that exhibit intelligent behavior by providing them with the ability to learn and reason. • Main tools: Probability theory, statistics, optimization, analysis of algorithms, numerical approximations, robotics, … Abilities Goals/Preferences Prior Knowledge Robot Observations Actions Environment

  3. Why build the SmartWheeler? • Potential to increase the mobility and freedom of individuals with serious chronic mobility impairments is immense. • ~4.3 million users of powered wheelchairs in the US (Simpson, 2008). • Up to 40% of patients find daily steering and maneuvering tasks to be difficult or impossible (Fehr, 2000). • An intelligent wheelchair platform provides opportunities to investigate a wide spectrum of cognitive robotics problems.

  4. The robot platform 1st generation (McGill) • Standard commercial wheelchair. • Onboard computer and custom-made electronics. • Sensors: laser range-finders, wheel odometers. • Communication: 2-way voice, touch-sensitive LCD. 2nd generation (Polytechnique)

  5. Software architecture Two primary components of cognitive robotic system: Interaction Manager and Navigation Manager

  6. Reinforcement learning paradigm Choose actions such as maximize the sum of rewards,

  7. Navigation management

  8. Autonomous navigation software

  9. Variable resolution robot path planning

  10. Interaction Management

  11. User interaction example

  12. Wheelchair Skills Test http://www.wheelchairskillsprogram.ca • Set of 39 wheelchair skills developed to test/train wheelchair users. • Each task graded for Performance and Safety on Pass/Fail scale. • Allows comparison and aggregation of results.

  13. Voice interaction with healthy subjects

  14. Voice interaction with target population

  15. Qualitative analysis • Positive • Impressed by autonomous functionality • Obstacle avoidance • Visual feedback • Negative • Wanted more time to familiarize with the system • Too much micromanagement • Microphone required on/off button

  16. Discussion • Current experimental protocol is constrained. • Useful for formal testing, inter/intra-subject comparison. • Limited use for measuring long-term impact. • Extension to standard living environments is possible. • Navigation in indoor living environments is possible. • Navigation in outdoor or large indoor environments is challenging. • Communication is reasonably robust for most subjects. • But suffers from lag, noise, and other problems. • Multi-modal interface is desirable but harder to design. • Need to investigate life-long learning for automatically adapting to new environments, new habits, and new activities.

  17. Project Team • McGill University: • Amin Atrash, Robert Kaplow, Julien Villemure, Robert West, Hiba Yamani • Ecole Polytechnique de Montréal: • Paul Cohen, Sousso Kelouwani, Hai Nguyen, Patrice Boucher • Université de Montréal • Robert Forget, Louise Demers • Centre de réadaptation Lucie-Bruneau • Wormser Honoré, Claude Dufour • Constance-Lethbridge Rehabilitation Centre • Paula Stone, Daniel Rock, Jean-Paul Dussault • Institut de réadaptation en déficience physique de Québec • François Routhier

  18. Reasoning and Learning Lab, SOCS

  19. Adaptive deep-brain stimulation Goal: To create an adaptive neuro-stimulation system that can maximally reduce the incidence of epileptiform activity.

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