Kaz Kawamura Center for Intelligent Systems Vanderbilt University
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Transcript of Kaz Kawamura Center for Intelligent Systems Vanderbilt University
From Intelligent Control to Cognitive Control:A Perspective from Cognitive Robot Engineering Point of View
Kaz Kawamura
Center for Intelligent SystemsVanderbilt University
Background
Our group have been working on a robotic system called ISAC (Intelligent Soft Arm Control) since late 1980s (as an industry-sponsored project.)
ISAC was initially developed as a robotic aid system using vision, voice and haptic-based adaptive control.
Background
Our group have been working on a robotic system called ISAC (Intelligent Soft Arm Control) since late 1980s (as an industry-sponsored project.)
Long-term goal was to develop an assembly “horon” (i.e. a cognitive co-worker) for intelligent manufacturing systems.
Background
Our group have been working on a robotic system called ISAC (Intelligent Soft Arm Control) for the last fifteen years.
ISAC was initially developed as a robotic aid system using vision, voice and haptic-based adaptive control.
Over the years, we gradually added hardware components and adopted a modular software development approach, i.e. multi-agent-based “hybrid architecture ( more like one Troy Kelly mentioned)”.
Background Our group have been working on a humanoid robotic system
called ISAC (Intelligent Soft Arm Control) for the last ten years. ISAC was initially developed as a robotic aid system using haptic-
based adaptive control.
In the last several years, we are adding computational modules to incorporate some of cognitive psychology (i.e. an central executive (A. Baddeley)) and neuroscience (i.e. an adaptive working memory (David Noelle))-based models to realize “cognitive control “ functionalities to ISAC.
Are these robots intelligent, cognitive or neither?
COG, MIT (Is COG the “Father of cognitive robots”?) ISAC, Vanderbilt Robonaut, NASA (Is it a vision of an ultimate
cognitive robot?) Many others shown by the workshop
participants (Rolf, Olaf, Owen, etc.)
Hypothesis
Artificial cognitive agents must share key features and “neurobiological and cognitive principles” (Jeff Krichmar) with humans if they are to become effective partners and coworkers in the human society.
Process of Cognitive (or Executive) Control
Human (and some animal) brain is known to process a variety of stimuli in parallel and choose appropriate action under conflicting goals. (Figure below was taken from: P. Haikonen, The Cognitive Approach to Conscious Machine, 2003)
Human Cognitive Control Functions
Ability of the brain to execute task and resolve conflicts
Focus on task context and ignore distraction Involves action selection and control where
reactive sensorimotor-based action execution falls short of task demands. Example: Stroop test
Modified from: Miller, E.K., Cognitive Control: Understanding the brain’s executive, in Fundamentals of the Brain and Mind, Lecture 8, June 11-13, 2003, MIT.
Cognitive Control
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NASA-JSC Robonaut Demo:“An Ultimate Cognitive
Robot?”
Key Features of Cognitive Robots(A Partial/Unproven/Controvertial List )
Ability to perceive the world in a similar way to humans (or better) (e.g., “active perception”, Olaf Sporns, “ecological approach to perception”, JJ Gibson)
Ability to develop cognition through sensorymotor coordination (e.g., “morphological computation”, Rolf Pfeifer)
Ability to communicate with humans using natural language and mental models (robust HRI such as overcoming the frame of reference problem, Alan Schultz)
Ability to have a sense of self awareness (internal model and machine consciousness, Igor Alexander, Owen Holland vs. Kevin O”Reagan)
Ability to use attention and emotion to control behaviors (cognitive control)
NASA’s Robonaut
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Concept of a Cognitive Robotic System
Adapted from a DARPA ITPO Program web site, 2003.
Working Definition
Cognitive Control for robots is the attention- and emotion-based robust sensory-motor intelligence to execute the task in hand or switch tasks under conflicting goals.
Action
Stimuli
Actuators
Sensors
Behavior 1 …Behavior N
…
Behaviors
LegendSES= Sensory EgoSpherePM= Procedural MemorySM=Semantic MemoryEM=Episodic MemoryCEA=Central Executive Agent
STMAttentionNetwork
SES
SM EM
LTM
PM
Self Agent
CEA
HumanAgent
Atomic Agents
PerceptionEncodings
Head Agent
Hand Agents
Arm Agents
WorkingMemorySystem
Completed
Currently being implemented
Cognitive Control on ISAC
Ability to use attention and emotion to control behaviors (i.e., cognitive control) is being implemented using the Sensory EgoSphere, the Attention Network, Emotion, the Working Memory System, the Central Executive Agent, and others.
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(chunks)
SES
to WMS
Stimuli
AttentionNetwork(Gating)
PerceptionEncodings
EmotionalSalience
Taskcommand
Current Work Current Work is aimed at testing
how modules involved in cognitive control work together as a system:
1. Working Memory System Training
[Poster Presentation by Stephen Gordon]
2. Situation-based Action Selection
1. Control Structure used during working memory system training
Experiment I: Working Memory Training for a Percept-Action Task
1. ISAC is trained to recognize specific objects
i.e., several colored bean bags.
2. ISAC is taught a small set of motion behaviors
i.e., reach, wave, handshake.
3. Bean bags are rearranged.
4. ISAC is asked to “reach to the bean bag”
(color is not specified).
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Experiment I
1. ISAC is trained to recognize specific objects ,i.e., several colored bean bags.
2. ISAC is taught a small set of motion behaviors ,i.e., reach, wave, handshake.
3. Bean bags are rearranged.
4. ISAC is asked to “reach to the bean bag” (color is not specified).
5. ISAC will attempt to load the relevant “chunks” into WMS for appropriate:
action to take (reach, wave, etc.) percept to act upon.
6. Over time, ISAC should learn which “chunk” (i.e., a percept-behavior
combination) is the most appropriate to choose
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Working Memory System Training
LTM
SES WM
Memory chunks
Candidate Chunks List
.
.
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LearnedNetworkWeights
Percepts
Experiment I (cont’d)
Sample configuration for reaching(top view)
Second sample configuration(top view)
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Experiment I - Video
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Learning Results for Reaching Action
Experiment II: Situation-Based Task Switching (Under Investigation)
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Music - pleasure
Alarm - annoyance
perform task?dance?
alarmed?ignore?
Barney - Task
Experiment II
A simulation experiment to test key system components for cognitive control using CEA, attention network, and emotion
A simple situation-based task switching using the Focus of Attention (next slide) is being
Music - pleasure
Alarm - annoyance
perform task?dance?
alarmed?ignore?
Barney - Task
Focus of Attention
Percept A
Percept B
Focus of Attention
Percept A
Percept B
Focus of Attention
Percept A
Percept C
Situation S2
Situation S1
Perceptualevent
Perceptualevent
Situation-based Action Selection (Under investigation)
Action A1
Action A2 Selected action(execution phase)
…
Updateprobabilities
Appropriate action
provided by human teacher(teaching phase)
][ )(ijAP
Situation Si
P1
P2
P1
P2
P1
P2
P3
P1
P2
P3
FOA FOA
P1,P2,P3 = percepts
Perceptualevent
Experiment II - Video
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Simulation Results
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What have we learned so far? Effectiveness of using a computational
neuroscience-based working memory model for perception-behavior learning on a robot (proof of concept)
Computational time of the WM software library is expected to grow exponentially as the robot accumulates experience (classical AI problem) (effective use of episodic memory?)
WM model does not seem effective for task switching
Needs a better mechanism than a FOA-based situational change for task switching (=> dynamic modeling of situations)
For further information, please visit our website at:http://eecs.vanderbilt.edu/CIS/
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Background
Our group have been working on a humanoid robotic system called ISAC (Intelligent Soft Arm Control) for the last ten years.
ISAC was initially developed as a robotic aid system using sensor-based intelligent control.
Human Agent
Self Agent
The key agent in our cognitive architecture is the Self Agent.
Minsky calles it the “Self Model” in his forthcoming book, The Emotion Machine.
Actually he uses the term “Self Models” which include both the Self Agent and the Human Agent in our architecture.
Behavior 1 …Behavior N
…
Behaviors
SESSM
EM
PM
Self Agent
STM LTM
HumanAgent
LegendSES= Sensory EgoSpherePM= Procedural MemorySM=Semantic MemoryEM=Episodic MemoryCEA=Central Executive Agent
Central ExecutiveAgent
DescriptionAgent
Anomaly DetectionAgent
Mental ExperimentAgent
Intention Agent
Activator Agent
Emotion Agent
AtomicAgents
First-orderResponse Agent
Completed
Currently being implemented
Not yet implemented
WorkingMemorySystem
Central Executive Agent (CEA):Robotic Frontal Lobes responsible for cognitive control functions
Inspired by the “central executive” from Baddeley’s working memory model (Baddeley, 1986)
Functions of CEA include Obtaining task sequence for task execution
Decision making
Action execution
Task monitoring
A. Baddeley, Working Memory, 11, Oxford Psychology Series, Oxford: Clarendon Press, 1986.
DecisionMaking
TaskExecution
Task-relatedPercepts
ResponseTo Percepts
FromInitial
Knowledge
FromEnvironment
Task executionsequences
Candidate TaskExecution Sequences
Selected TaskExecution Sequences
Action
Feedback
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Questions
1. How could cognitive control be implemented in robotics? (model or no model?)
2. How does one know when a robot becomes a cognitive robot?
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