Cognitive Control within a Parallel Distributed MultiAgent-Based Cognitive Robot Architecture:

From Body-Sensor Integration to Body-Mind Integration

New Challenge

Integration of Body and MindIntegration of Body and Mind

Cognitive Robot Cognitive Robot ††

Conscious RobotConscious Robot

† K. Kawamura, D. Noelle, et al., A Multi-Agent Approach to Self-Reflection for Cognitive Robots”, the 11th International Conference on Advanced Robotics, Coimbra, Portugal, June 30 – July 3, 2003.

MultiAgent-Based Cognitive Architecture for ISAC

MultiAgent-Based Cognitive Architecture for ISAC

Compound AgentsCompound Agents Self Agent – Representation of Robot’s SelfSelf Agent – Representation of Robot’s Self Human Agent – Representation of HumansHuman Agent – Representation of Humans

Memory StructuresMemory Structures STM – Sensory EgoSphereSTM – Sensory EgoSphere LTM – Procedural memoryLTM – Procedural memory WM – WM – under constructionunder construction

Four Key Cognitive Abilities

We assume that there are four key cognitive abilities cognitive robots must have in order to become effective members of human society:

1. Self-awareness System Health Monitoring Task Monitoring

2. Awareness of others Intention detection Mutual Spatial & Timing Control

3. Cognitive Control Executive Controller Working Memory

4. Self-reflection Ability to reflect experiences gained Ability to think ahead

Self Agent

Human Agent

Mental Experimentation Agent

Central Executive Controller

Self AgentSelf Agent with memory structures

Pronoun Agent

Description Agent

Anomaly Detection Agent (ADA)

Mental Experiment Agent (MEA)

WM

Human AgentSelf Agent

Intention Agent

Interaction Agent

STM LTM

SES

PM

PM

Central Executive Controller

Atomic Agents

Meta Level Behaviors

DM

DM

ISAC Memory Structure

SES=Sensory Ego-SpherePM= Procedural MemoryDM=Declarative Memory

Levels of Monitoring and Action within the Self Agent

Levels of Interaction within the Human Agent

New Architecture for ISAC

STM: Sensory EgoSphere (SES)††

Purpose of the SESPurpose of the SES Spatio-temporal method for Spatio-temporal method for

storing sensory eventsstoring sensory events Integration of multiple sensory Integration of multiple sensory

modalitiesmodalities Short-term memory of eventsShort-term memory of events Egocentric, topological Egocentric, topological

mapping of locationsmapping of locations

R.A. Peters II, K.A. Hambuchen, K. Kawamura, and D.M. Wilkes, "The Sensory Ego-Sphere as a Short-Term Memory for Humanoids", Proc. IEEE-RAS Int'l. Conf. on Humanoid Robots, Waseda University, Tokyo, Nov 22-24, 2001, pp. 451-459.

SMC-STM Experiment - Object Learning from Human Pointing

ISAC is taught to reach 5 objects on a table

SMC-STM Experiment - Object Learning from Human Pointing

Data Flow for the human-guided motion generation and behavior derivation demo

SMC-STM Experiment - Object Learning from Human Pointing

Robot learns objects upon human instruction

Scans table for human pointing finger

Registers object on Sensory EgoSphere (SES)

Robot retrieves position of objects at human direction

Parses request Retrieves object information

from SES Looks and/or points to object

of interest

• Navigation based on Egocentric representations

• SES represents the current perception of the robot

• LES represents the expected state of the world

• Comparison of these provides the best estimate direction towards a desired region

LES

SES

SES- and LES-Based Navigation

K. Kawamura, R.A. Peters II, D.M. Wilkes, A.B. Koku and A. Sekman, “Towards Perception-Based Navigation using EgoSphere”, Proc. of the International Society of Optical Engineering Conference (SPIE), October 28-20, 2001.

Novel Approach: Range-free perception-based navigation

Demo

PM stores motion skills such as how to reach to a point

PM is a combination of primitive motions which are generated by several motion generation techniques

Interpolation method we are using is called the Spatio-Temporal Isomap

LTM database currently contains Primitive Memory (PM)

Long-Term Memory (LTM)

Behavior Generation and Learning

Basic behaviors are derived Basic behaviors are derived from the motion taught by from the motion taught by humanhuman

Spatio-temporal Isomap is Spatio-temporal Isomap is used to derive primitive and used to derive primitive and meta-level behaviorsmeta-level behaviors

Behaviors are stored in Behaviors are stored in Procedural Memory(PM) in Procedural Memory(PM) in Long Term Memory of robotLong Term Memory of robot

Isomap Method †† for Behavior-Based Motion Generation

†† J . B. Tenenbaum, V. de Silva, and J. C. Langford,”A global geometric framework for nonlinear dimensionality reduction”, Science, 290(5500), pp. 2319–2323, 2000.

Behavior Generation and Learning (cont’d)

Structure of LTM database including steps to generate behaviors

Verbs and Adverbs

Method of generating a wide variety of behaviors from a few exemplar motions

First used in animation community º

Similar to primitive motion modules

º C. Rose, M. F. Cohen and B. Bodenheimer, “Verbs and adverbs: multidimensional motion interpolation”, IEEE Computer Graphics and Applications, 18:5, Sept-Oct 1998, 32–40.

Verbs and Adverbs (con’t)

ISAC performing a reaching motion using the verbs and adverbs technique.

ISAC preparing to grasp Barney

Verbs and Adverbs (con’t)

Verbs General description of

motions Joint angles as

function of time Adverbs

Parameters of a verb A verb can have any

number of adverbs Adverbs are

independent of each other

Can be subjective values such as sad or happy

Working Memory (WM)

Maintains transient information that is critical for decision-making in current context

Allows one to remember a phone number - retaining it only long enough to dial it

Of particular interest are the lateral portions of the prefrontal cortex (PFC)

PFC

Schematic diagram of the visual DA model; mechanisms in this example detect shape or color to drive the desired visual response

1. PFC: http:www. driesen.com/prefrontal_cortex.htm

2. A. Cohen and U. Feintuch, “The dimensional-action system: a distinct visual system,” Common Mechanisms in Perception and Action: Attention and Performance XIX. W. Prinz and B. Hommel, Eds. New York: Oxford University Press, 2002.

Cognitive Control and the CEC

Cognitive Control in the human brain:

The anterior cingulated cortex (ACC) is responsible for executive or cognitive control in our brain

ACC helps focus attention and select actions

Central Executive Controller (under construction) executes tasks based on:

Attention system supervision Memory management Context processing Non-routine behavior

selection/control

Goal Oriented Perception-Action Mapping

STM LTMWM

CEC

Gating

PerceptionEncoding

Stimuli

ActionDecision Actuators Action

Command

Data flow

Control flow

Cognitive Experiment

Demonstrates how the CEC, STM, WM and LTM work together to carry out a cognitive task

CEC

WM

LogicalInferenceEngine

Motionbuffer

MotionInterpolator

Objectrepresentation

IntentionAgent

Arms

PM

PM

PM

LTM

LTM Manager

Feedback

Command

Human

Task planner

Stimuli

STM

SES

ContextAnalyzer

weight

Color module

Shape module

Task

Attention Network

Action

Cognitive Control and the CEC

The Central Executive Controller (CEC) , currently under construction, is responsible for the cognitive control during action decision

A basic CEC is designed to provide the following functions:

Controls task contexts via gating Allows reactive processes to process tasks whenever

possible Provides deliberation when reactive processes cannot

handle the task by themselves Allows access to self reflection when impasses arise

Cognitive Experiment -Stroop Experiment

RED BLUE

RED YELLOW

GREEN YELLOW

YELLOW RED

BLUE GREEN

GREEN BLUE

A word and color are shown, then the subject must name the color in which a word is written

Modified Stroop Experiment

RED

BLUE

BLUE

RED

Robot is told to choose a particular color/ word (e.g., red “BLUE”)

SES maintains different representations of the environment for color and word (Note: Color recognition is VERY sensitive to environmental lighting conditions)

These are held in WM after passing through the attention system

RED

BLUE

BLUE

RED

Modified Stroop Experiment (Cont’d)

“Point to red BLUE”

Send attention parameters

Attention systemfilters data

Human

CEC

Attention

Store filtered dataWM

Retrieve motionsCEC

Perform ActionCEC

Word BLUE

Color red

Point to BLUE

Self-Reflection

Self Reflection here is defined as a cognitive activity of a robot to:

Reflect on past experiences Control intentional actions

We have not yet finalized the exact specifications nor the roles of the Self Reflection Agent into a multi-agent architecture. Possible roles could include:

Reflective process may inspect the LTM database

Modify this database to generate new behaviors

Reflective Processes in ISACA Proposal

We propose to take a multi-strategy approach: reflection for an impasse and reflection on behavior.

Three proposed agents, interacting with/within the Self Agent, implement ISAC’s reflective capabilities:

The Anomaly Detection Agent (ADA), during normal operation, gathers information about the distribution of inputs and outputs for each agent

The Mental Experimentation Agent (MEA) is invoked upon impasse. It conducts a search through the space of control parameters for those which are expected to resolve the impasse

The Central Executive Controller (CEC) Agent

Knowledge Base

facts & rules facts & rulesabout the domain about reasoning processes

Working Memory

goals & intentions plans

relevant informationabout past states/potential future states

ReflectiveProcesses

Representations of the Current State

Actions to be Taken Now

Reflective Process

Concept for The Anomaly Detection Agent(ADA)

Passively record the input/output distributions of each agent, conditioned on the current intention. This enables the identification of agents operating

outside of their normal operating range.

Self Agent

ADA

ObservedAgent

Inputs Outputs

Current Intention

Concept for The Mental Experimentation Agent(MEA)

When the Self Agent recognizes an impasse, it activates the MEA

The MEA searches for control signals which will resolve the impasse

Agents are run in “simulation mode” in order to determine the likely effects of control signal changes

The search is guided to suspected agents by the ADA

MEA

SuspectedAgent

Inputs Outputs

Control

Conclusion

During the past decade, we have seen major advances in the integration of body and sensor.

The next grand challenge will be in the integration of body and mind.

An adaptive working memory, an executive controller , and neuron-base brain processing may be key to this integration.

ISAC: System Information

Interaction competencies for a personal robot

Hardware Software

OBSERVATIONAL

Presence of Person: Infrared (IR)

Passive IR motion detector array

Digital I/O

Sound: Event Localization Condenser Microphones Matlab

Speech: Detection/Recognition Handheld Microsoft Speech Recognition Engine 4.0

Vision: Face and Gesture Sony Color CCD Cameras Visual C++ routines, some with Intel Libraries

DEMONSTRATIVE / RESPONSIVE

Speech: Synthesis PC Speakers AT&T Natural Voices Engine

Motor behaviors: Head Directed Perception PTU-46-70 IMA wrapper for serial port

Motor behaviors: Arms Pneumatic Muscles Visual C++ routines and control

Intelligent Machine Architecture

Hardware or Resource Agent interfaces to sensor or actuator

hardware

Behavior or Skill Agent encapsulates basic behaviors or

skills

Environment Agent provides an abstraction for

dealing with objects in the robot’s environment

Sequencer Agent performs a sequence of

operations

Multi-Type Agent combines the functionality of at

least two of the first four agent types

RightCamera

LeftCamera

Pan-TiltHead

LeftArm

LeftHand

RightArm

RightHand

Block Beanbag

LegendHardware

AgentEnvironment

Agent

Spoon Cup

IMA is a multi-agent based robot control architecture developed at Vanderbilt. Atomic agents (e.g. hardware, behavior,environment) are the building blocks of IMA.