Toward Versatile Robotic Assistants for Security and Service Applications
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Transcript of Toward Versatile Robotic Assistants for Security and Service Applications
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Toward Versatile Robotic Assistants for Security and Service Applications
Monica N. Nicolescu
Department of Computer Science and Engineering
University of Nevada, Reno
http://www.cs.unr.edu/~monica
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Overview
Goals:
Integrate robots in human society
Increase the utility of autonomous robots and their ability to
function in dynamic, unpredictable environments
Facilitate interaction with robots
Motivation:
Accessibility to a large range of users
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Application Domains
Security
Scenario: security checkpoint
Task: threat detection
Service
Scenario: office/home robot assistant
Task: service multiple user requests
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Research Problems
Robot control:
Support for frequent human-robot interactions, include the
human in the loop
Communication:
Engage in sustained interactions with people
Express/understand intent
Learning:
Program robots using an accessible method
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Approach
Behavior-based control with a particular behavior
representation Frequent, sustained interactions with people
Understanding intent
Learning by demonstration Natural robot programming
Understanding (malevolent) activity/intent
Communication through actions Expressing intent
Long-term: integrate with neuroscience and cognitive
science approaches
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Robot Control
A control architecture that provides support for frequent
human-robot interactions
Modularity, robustness and real-time response, support
for learning
Automatic reusability of existing components
Ability to encode complex task representations
Run-time reconfiguration of controllers
Behavior-based control as underlying control
architecture
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Behavior-Based Robot Control
Behaviors Goal-driven, time-extended control processes, running in
parallel, connecting sensors and effectors
Highly effective in unstructured, dynamic environments
Usually invoked by reactive conditions
Built-in task specific information
BehaviorInput ActionsSensors Actuators
Behavior
Inp
ut
Input Actions
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Hierarchical Abstract Behavior Architecture
Extended behavior-based architecture
Representation & execution of complex, sequential,
hierarchically structured tasks
Flexible activation conditions for behavior reuse
Representation of tasks as (hierarchical) behavior
networks
Sequential & opportunistic execution
Support for automated generation (task learning) Environment
sensory input
M. N. Nicolescu, M. J Matarić, “A hierarchical architecture for behavior-based robots", International Conference of Autonomous Agents and Multiagent Systems, July 15- July 19, 2002.
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The Behavior Architecture
Goals Behi
{1/0}
Abstract/primitive behaviorstructure
Primitive behavior
Perform actions
Abstract behavior
Test world preconditions
Task specific preconditionsif met
Goals Beh1…k {1/0} Abstract Behavior Embeds representations of the
preconditions & goals
Primitive Behavior Performs actions, achieve goals
Representation and execution of behavioral sequences
Flexible activation conditions behavior reuse
Representation of tasks as behavior networks
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Task Representation: Behavior Networks
GoTo(Source)
PickUp(Box)
Follow(Wall)
GoTo(Dest)
Drop(Box)
A
A
A
A
A
A
A
A
A
A
Abstract behaviors
Primitive behaviors
Links represent task relevant precondition-postcondition dependencies
Permanent preconditionEnabling preconditionOrdering precondition
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Layers of Abstraction: Network Abstract Behaviors
Abstracts existing networks into a single component
Use NAB’s as parts of other behavior networks
Allows for a hierarchical representation of increasingly complex tasks
Upon activation, enable their components
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The Robot Testbed
Pioneer 2DX mobile robot Pan-tilt-zoom camera Laser range-finder Gripper 2 rings of 8 sonars PC104 stack Logitech cordless headset IBM ViaVoice speech software Implementation in Ayllu
Picking up, dropping objects (PickUp, Drop) and tracking targets (Track)
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Experimental Validation
Sequential & opportunistic execution Object transport &
visit targets subtasks
Hierarchical representation
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Results
Yellow Orange Pink Light-GreenTrial 5
Yellow Light-Green Orange PinkTrial 4
Light-Green Yellow Orange PinkTrial 3
Pink Yellow Orange Light-GreenTrial 2
Orange Pink Light-Green YellowTrial 1
Order of target visits
M. N. Nicolescu, M. J Matarić, “A hierarchical architecture for behavior-based robots", International Conference of Autonomous Agents and Multiagent Systems, July 15- July 19, 2002.
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Human-Robot Interaction – Proposed Work
Goal:
Include support for frequent human-robot interactions
Issues:
Handle interruptions
Switching between different activities (idle, task execution,
learning, dialog)
Approach:
Incorporate awareness of human presence
Incorporate a model of activity control (situations and associated
responses) with our behavior based architecture
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Communication – Proposed Work
Goal:
Understanding intent from simple behavior
Approach:
Match high-level perceptions of the robot with the known
goals of the robot’s behaviors
Applications:
Service: achieve better cooperation by understanding
human intentions (e.g., giving/taking a tool/object)
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Learning
Learn a high-level task
representation, from a set of
underlying capabilities
already available to the robot
Approach:
Learning by experience (teacher following) Active participation in the demonstration
Mapping between observations and the skills that achieve the same
observed effects
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Learning by Demonstration Framework
Inspiration:
Human-like teaching by demonstration
Multiple means for interaction and learning: concurrent use of
demonstration, verbal instruction, attentional cues, gestures, etc.
Solution: Instructive demonstrations, generalization and practice
GIVEDEMONSTRATION
TASK REPRESENTATION
FIRST?YES
EXECUTETASK
OK?YES
DONE
NONOGENERALIZE
GENERALIZED REPRESENTATION
EXECUTETASK
REFINED TASK REPRESENTATION
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Instruction Stage: Teacher’s Perspective
The teacher is aware of:
Robot skills
What observations/features the robot could detect
Instructions for the robot
Informative cues:
“HERE” – moments of time relevant to the task
The teacher may give simple instructions:
“TAKE”, “DROP” – pick-up, drop objects
“START”, “DONE” –beginning/end of a demonstration
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Instruction Stage: Learner’s Perspective
Teacher-following strategy (laser rangefinder &
camera)
Abstract behaviors (perceptual component)
continuously monitor their goals:
Ability to interpret high-level effects (e.g. approaching a target,
being given/taken an object)
Goals Met Abstract Behavior signals
observation-behavior
mapping
Compute the values of behavior parameters gathered
through its own sensors
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Learning an Object-Transport Task
Human demonstration
Robot demonstration
Learned topologyEnvironment
All observations relevantNo trajectory learning
Not reactive policy
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Generalization
Hard to learn a task from only one trial: Limited sensing capabilities, quality of teacher’s demonstration,
particularities of the environment
Main learning inaccuracies: Learning irrelevant steps (false positives)
Omission of steps that are relevant (false negatives)
Approach: Demonstrate the same task in different/similar environments
Construct a task representation that: Encodes the specifics of each given example
Captures the common parts between all demonstrations
M. N. Nicolescu, M. J Matarić, ”Natural Methods for Robot Task Learning: Instructive Demonstrations, Generalization and Practice", Second International Joint Conference on Autonomous Agents and Multi-Agent Systems , July 14-18, 2003
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Generalization
Task: Go to either the Green or Light Green targets, pick up the Orange box, go between the Yellow and Red targets, go to the Pink target, drop the box there, go to the Light Orange target and come back to the Light Green target
None of the demonstrations corresponds to the desired task Contain incorrect steps and inconsistencies
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Generalization Experiments
3rd Human demonstration
Robot performance
3rd 2nd 1st
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Refining Task Representation Through Practice
Practice allows more accurate refining of the learned tasks Unnecessary task steps (“bad”) Missing task steps (”come” ”go”)
A
B
A C
Deleteunnecessarysteps
A
B
A C
M
N
Include newlydemonstratedsteps
A
C
B
F
A C
BAD
BAD
COME
M
N
GO
A
B
A C
M. N. Nicolescu, M. J Matarić, ”Natural Methods for Robot Task Learning: Instructive Demonstrations, Generalization and Practice", Second International Joint Conference on Autonomous Agents and Multi-Agent Systems , July 14-18, 2003
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Practice and Feedback Experiments
3rd demonstration Practice run & feedback
Robot performance
Topology refinement
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Practice and Feedback Experiments
Practice run & feedback1st demonstration
Robot performancePractice run
Topology refinement
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Humandemonstration
Robotexecution Learned network
Gate Traversing Task
Learning from Robot Teachers
M. N. Nicolescu, M. J Matarić, "Experience-based representation construction: learning from human and robot teachers", IEEE/RSJ International Conference on Intelligent Robots and Systems, Pages 740-745, Oct. 29 – Nov 3, 2001
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Learning – Proposed Work
Goal: learn a larger spectrum of tasks
Repetitive tasks: “repeat-until”
Conditioned tasks: “if-then”
Time relevant information: “do-until”
Trajectory learning: Turn(Angle), MoveForward(Distance)
Approach: Use an increased vocabulary of instructional cues (repeat,
until, if, etc.)
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Support for Communication – Proposed Work
Goal:
Understanding intentions from complex activity
Approach:
Use learning from demonstration to train the robot patterns of
activity, and
Understand activity by observing/following people and mapping the
observations to its learned database of activities
Applications:
Security: detect suspicious behavior (e.g. passing around a
checkpoint area)
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Communicating Through Actions – Proposed Work
Goal:
Natural communication & engaging in interactions
with people
Approach:
Use actions as vocabulary for communicating
intentions
Understanding exhibited behavior is natural: actions
carry intentional meanings
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Communication – Preliminary Work
If in trouble, try to get help from a human assistant perform “dog-like” actions to get a human’s attention perform the actions that failed in front of the helper to express
intentions
Traverse a blocked gate
Pick up an inaccessible object
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Communication – Proposed Work
Goal:
Understanding intent from interaction
Approach: Engaging in interactions with people can expose
underlying intentions
Applications: Security: uncooperative person could potentially have
malicious intentions Service: learn about cooperative/uncooperative users
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Summary
Proposed framework for the development of
autonomous, interactive robotic systems
Behavior-based control with a particular behavior
representation Frequent, sustained interactions with people
Understanding intent
Learning by demonstration Natural robot programming
Understanding (malevolent) activity/intent
Communication through actions Expressing intent, engaging in interactions with people