Cognitive Robotics: Lessons from the SmartWheeler project

Joelle Pineau, jpineau@cs.mcgill.ca

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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t= 0

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⎣ ⎢

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⎦ ⎥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.