Robotics for Intelligent Environments
Manfred Huber
huber@cse.uta.edu
March 2002
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Robotic Applications in Smart Homes
Control of the physical environment
Automated blinds
Thermostats and heating ducts
Automatic room partitioning
Personal service robots
House cleaning
Lawn mowing
Assistance to the elderly and handicapped
Office assistants
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What is a Robot ?
Robota (Czech) = A worker of forced labor
Japanese Industrial Robot Association:
A robot is a device with degrees of freedom that
can be controlled
Historical Robots include:
Mechanical automata
Motor-driven automata
Computer-controlled robots
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Traditional Robotics
Industrial robot manipulators
Repetitive tasks
High speed
Few sensing operations
High precision movements
Pre-planned trajectories and task policies
No interaction with humans
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Robots in Smart Home Environments
Problems of Traditional Robotics:
No sensing
Can not handle uncertainty
No interaction with humans
Reliance on perfect task information
Complete re-programming for new tasks
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Current Robots
Design Goals:
Sensor-rich
Flexible
Versatile
Controllable
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Future Home Robots ?
© Peter Menzel / MIT AI Lab
© Honda Corp
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Challenges for Robots in Intelligent
Environments
Control Challenges:
Autonomy in uncertain environments
Adaptation and Learning
Human-machine interaction
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Uncertainty in Robot Systems
Sensor Uncertainty:
Sensor readings are imprecise and unreliable
Non-observability:
Various aspects of the environment can not be observed
The environment is initially unknown
Action Uncertainty:
Actions can fail
Actions have nondeterministic outcomes
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Behavior-Based Robots
Behavior is achieved by combining "reflexes":
Achieves reactivity
Avoids world models
Tight coupling of sensors and actions
© MIT AI Lab
March 2002
© MIT AI Lab
http://www-robotics.usc.edu/~maja/robot-video.mpg
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Probabilistic Robotics
Explicit reasoning about
Uncertainty using Bayes
filters:
b(st )   p(ot | st )  p(st | st 1, at 1 ) b(st 1) dst 1
Used for:
Localization
Mapping
Model building
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Hybrid Control Systems
Abstract Planning and Policy
Formation Layer
Goal-directed task performance
Permits sophisticated reasoning
Reactive Behavior Layer
Ensures basic autonomy
Provides reactivity
Reduces complexity in the
planning layer
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Hybrid Control Policies
Finite State Rotation Policy:
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How Many Roboticists does it take to
change a Lightbulb ?
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Adaptation and Learning in Robots
Adaptation of Existing Control Policies
Adaptation to changing environments
Adjustment to new user preferences
Learning New Policies
Full autonomy in remote environments
Dynamic extension of task repertoire
Learning Sensor Interpretations
Reduction in the amount of data
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Learning Sensory Patterns
Learning to Identify Features
Example Learning Techniques:
Neural Networks
Kohonen Maps
Unsupervised Clustering
Decision Tree Induction
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Chair
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Learning Control Policies
Learning to make Rational Decisions
Challenges:
Learning without supervision
Learning in uncertain environments
Learning from Human-Machine interaction
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Reinforcement Learning
Learning Control Policies from Reward Signals
Does not require knowledge of the correct policy
Can deal with intermittent, sparse feedback
Q-learning:
Learning an optimal utility function, Q(s, a), for a Markov
Decision Processes
Q(st-1, a)
r + g maxb Q(st, b)
Does not require a model
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Learning in Hybrid Control Systems
Policy Acquisition Layer
Learning tasks without supervision
Discrete Event Model Layer
Learning a system model
Basic state space compression
Reactive Behavior Layer
Initial competence and reactivity
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Example: Learning to Walk
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Hierarchical Skill Acquisition
Developing Skills Hierarchically
Simplified control policies
Increasingly abstract state spaces
Better learning performance
Hierarchical Reinforcement
Learning
Learning with abstract actions
Acquisition of abstract task
knowledge
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Personal Service Robots
Control Challenges:
http://www.cs.cmu.edu/~thrun/movies/pearl-assist.mpg
Robustness requirements
Safety and reliability requirements
Interaction with humans
Human-Machine interfaces
Application Domains:
Office assistants
Home cleanup
© CMU Robotics Institute
http://www/cs/cmu.edu/~thrun/movies/pearl_assist.mpg
Assistance to elderly and handicapped
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Human-Machine Interfaces and Variable
Autonomy
Variable Autonomy
Autonomous operation / learning
User operation / teleoperation
Behavioral programming
Following user instructions
Imitation
Potential Interfaces:
Keyboard
Voice recognition
Visual observation
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Human-Machine Interfaces : Teleoperation
Remote Teleoperation
Direct operation of all degrees of
freedom by the user
Simple to install
Removes user from dangerous areas
Can be exhaustive
Requires insight into the mechanism
Easily leads to operation errors
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"Social" Interactions with Robots
"Attentional" Robots
Focus on the user or task
First step to imitation
"Emotional" Robots
Better acceptance by the user
More natural human-machine
interaction
© MIT AI Lab
http://www.ai.mit.edu/projects/cog/Video/kismet/kismet_face_30fps.mpg
Users are more forgiving
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Summary
Robots in Intelligent Environments require:
Autonomous Control
Adaptation and Learning Capabilities
Flexible Human-Machine Interfaces
Versatile Mechanisms
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