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Cognitive Robotics: Lessons from the SmartWheeler project
Joelle Pineau, [email protected]
School of Computer Science, McGill University
September 22, 2010
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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, …
RobotRobot
EnvironmentEnvironment
AbilitiesGoals/Preferences
Prior Knowledge
ActionsObservations
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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.
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The robot platform
• Standard commercial wheelchair.
• Onboard computer and custom-made electronics.
• Sensors: laser range-finders, wheel odometers.
• Communication: 2-way voice, touch-sensitive LCD.
1st generation (McGill)
2nd generation (Polytechnique)
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Software architecture
Two primary components of cognitive robotic system:Interaction Manager and Navigation Manager
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Reinforcement learning paradigm
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E rt
t= 0
T
∑ ⎡
⎣ ⎢
⎤
⎦ ⎥Choose actions such as maximize the sum of rewards,
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Navigation management
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Autonomous navigation software
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Variable resolution robot path planning
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Interaction Management
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User interaction example
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Wheelchair Skills Test
• 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.
http://www.wheelchairskillsprogram.ca
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Voice interaction with healthy subjects
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Voice interaction with target population
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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
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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.
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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
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Reasoning and Learning Lab, SOCS
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Adaptive deep-brain stimulation
Goal: To create an adaptive neuro-stimulation system that can maximally reduce the incidence of epileptiform activity.
