lecture 6. cognitive robot...

EE788 Robot Cognition and Planning, Prof. J.-H. Kim

Lecture 6. Cognitive Robot ArchitectureLecture 6. Cognitive Robot Architecture

Robot Intelligence Technology Lab.

Contents

1. INTRODUCTION2. MULTIAGENT-BASED COGNITIVE ROBOT ARCHITECTURE

- Reflective Processes- Cognitive architecture- Self Agent- Machine Consciousness

3. MEMORY STRUCTURE- Short-Term Memory: The Sensory EgoSphere

2Robot Intelligence Technology Lab.

- Short-Term Memory: The Sensory EgoSphere- Long-Term Memory: Procedural, Episodic, and Declarative Memories- Working Memory

4. COGNITIVE CONTROL AND CENTRAL EXECUTIVE AGENT- Cognitive Control- Central Executive Agent

5. CURRENT COGNITIVE CONTROL EXPERIMENT6. CONCLUSIONS

1. INTRODUCTION

n The new generation of robots should be able to recognize and deal with situations in which its traditional reactive and reasoning abilities fall short of meeting complex task demands.

n A cognitive robot as a robot that knows what it is doing and reflects on past experience to deal with new situations.


n Cognitive control in humans is the ability to “consciously manipulate thoughts and behaviors using attention to deal with conflicting goals and demands”

1. INTRODUCTION

n Cognitive modeling: approaches and architecturesl Neural network and symbolic approaches

n Computational modelsl Low-level cognitive processes: perception, attention and

memory


l Higher-level cognitive processes: language and reasoning

n Major issues and controversiesl Should computational models be thought of as theories?

l Is the architecture of cognition symbolic or subsymbolic?

l How is the world reflected in the mind?

1. INTRODUCTION

n Why emotions?l Rationality and emotionality are not opposed or

even contradictory. Emotionality is a prerequisite of rational behavior.

l Humans treat computers like persons. If people have emotional relations to computers, why not


have emotional relations to computers, why not make the computers recognize these emotions or make them express emotions?

l In computer animation, to give synthetic actors some autonomy, personality models have to be developed in which emotions play a major role.

1. INTRODUCTION

n Cognitive humanoid robot, ISACl Cognitive robot architecture: three distinctive memory structure

- Short-term memory- A sparse data structure called “Sensory Egosphere”

containing spatio-temporal sensory data acquired within a recent time frame


- Long-term memory- Composed of behaviors, semantic knowledge and

past experience- Working memory

- Allows attention on the most relevant features of the current task

1. INTRODUCTION


Fig 1.1 Robotic system with structured intelligence

1. INTRODUCTION


2. MULTIAGENT-BASED COGNITIVE ROBOT ARCHITECTURE


2.1 Introduction

n Three key cognitive abilities a robot must have in order to effectively interact with humans:l Self-awareness: the robot must be able to reason about

the status of its own internal processing.

l Awareness of other: the robot must be able to reason about


l Awareness of other: the robot must be able to reason about the states of the humans with which it interacts.

l Self-reflection: it allows the robot to reason about its own abilities, cognitive processes, and knowledge.

2.1 Introduction

n Earlier hybrid architectures to build machines capable of perceiving and interacting with the world around them:l explicit, often logical, knowledge representation schemes and

formally justified techniques for manipulating internal representations. l dynamic, and sometimes complex, interactions between modular

primitive reactive processes and the world.à Robots being both fluent in routine operations and capable of


à Robots being both fluent in routine operations and capable of adjusting their behavior in the face of unexpected situations or demands.

n A fully cognitive robot should be able to recognize situationsin which its reactive and reasoning abilities fall short of meeting task demands, and it should be able to make reasoned modifications to those abilities in hopes of improving the situation.

n DARPA has recently announced a new research initiative in cognitive systems – systems that possess levels of autonomy and reasoning far beyond those of today’s systems. l Cognitive systems will literally be systems that know what

they are doing.They will include both reactive and deliberative processes and

2.1 Introduction


l They will include both reactive and deliberative processes and will also incorporate mechanisms for self-reflection and adaptive self-modification (see Figure 2.1).

n A humanoid robot ISAC (Intelligent Soft-Arm Control)l To work with humans as an assistant in a variety of settingsl A multi-agent software architecture for parallel and distributed

robot control and a robust human-robot interface

2.1 Introduction


Figure 2.1 A Cognitive System Architecture

2.1 Introduction

n The development and maintenance of complex orlarge-scale software systems can benefit from domain-specific guidelines that promote code reuse and integration through software agents.

n Information processing in ISAC


n Information processing in ISACl From perception through action executionl Integrated into a multi agent-based software architecture

based on the Intelligent Machine Architecture (IMA)l The IMA was designed to provide such guidelines and

allows for the development of subsystems capable of environmental modeling and robot control through the collections of IMA agents and associated memories.

2.2 Reflective Processes

Reflection in Multi-Agent Systems

n Reactive systems:l Collections of interacting reactive agents can adapt in

a sometimes surprising way to varying environments.

l The systems often rely on the emergence of useful dynamic


patterns from the complex interaction of simple components.

l Quite difficult to discover a functional decomposition and a coupling of agents that reliably produces adaptive behavior across a wide range of contexts.


n Deliberation processesl Common sentential knowledge representation schemes

support the design process - specific bits of knowledge (e.g., rules) can be incorporated without danger of catastrophic interactions with the rest of the knowledge base.


base.

l While deliberation may be too inefficient to guide the system through stereotyped behaviors, it does provide many benefits when circumstances are unusual and unexpected.


n Reflective processesl reason about the internal information processing processes

of the system and the knowledge and mechanisms that guide the selection of actions.

l Note that deliberative components of the system reason about the external world and the actions that can be taken in it.

l A cognitive system will need to perform such reflective


l A cognitive system will need to perform such reflective reasoning, analyzing and modifying its own reactive and deliberative mechanisms.

l Reflection allows a system to "know what it knows" and, potentially, to "know what it needs to find out.“

l Reflective processes can both guide autonomous learning and provide a means for performance improvement through interaction with humans.


n Multiple software agents based architecturel A greater challenge with regard to a reflective process's ability

to reason about the way in which the collection of agents collectively process information.

l Placing critical knowledge in shared databases addresses part of this challenge.


n Ways to address this challenge, characterized by the answers to the following interrelated design questions:l HOW/WHAT/WHEN does the reflective process come to know

about the information processing behavior of agents and collections of interacting agents?

Self-Reflective Processes in ISAC

n Reflective processes, interacting with the Self Agent, will continuously gather statistics about the distribution of inputs and outputs observed for each individual agent.

n When an impasse arises, these statistics will be used as heuristic guides for identifying the potential source of the problem.


n At the point of impasse, reflective processes will experiment with the existing collection of agents, running them in “simulation mode” in order to predict the likely outcome of various control manipulations.

Self-Reflective Processes in ISAC

n These predictions will then be fed into a utility-based analysis in order to select a new collection of control parameters, which will then be used to address the impasse.

n Finally, the reflective processes will compare the actual behavior of the system to that which was “simulated” during experimental


of the system to that which was “simulated” during experimental reflection, recording statistics on this accuracy for use when estimating the reliability of future simulations.

2.3 MULTIAGENT-BASED COGNITIVE ROBOT ARCHITECTURE


ISAC’s Cognitive Architecture

n The Intelligent Machine Architecture (IMA)l A multi-agent based software architecture

l An agent-based software system that permits robot control through collections of cooperating software agents

l Information processing in ISAC, from perception through action selection and execution, is integrated into.


action selection and execution, is integrated into.

l For the development of subsystems capable of environmental modeling and robot control through the fabrication of collections of IMA agents and associated memories.

l An architecture for concurrently executing IMA agents on separate machines that perform as a group through extensive inter-agent communication.



Figure 2.2 Multi-Agent Based Cognitive Architecture


n Atomic IMA agentsl Autonomous entities that have one or more execution

threads.l Typically, an atomic agent cannot perform useful activity

independently. l Collections of IMA agents interact to complete tasks

by providing capabilities, such as sensing the external


by providing capabilities, such as sensing the external environment.

n Various types of IMA agents for robot controll Hardware agents for accessing sensors and actuators, environment

agents for abstracting object and environment interactions. l Within IMA, the robot itself is abstracted as a self-agent (SA), and

the state of external entities, such as people, is abstracted in the form of human agents.


n A Human Agent and Sensory EgoSphere (SES) l To represent the external environment

n The Self Agent and the Long Term Memory (LTM) l To provide memory and internal control mechanisms


for ISAC.l The self-agent uses a memory database structure,

consisting of short-term and long-term structures, to determine the appropriate situational control commandsfor the robot.

2.4 The Human Agent

n The Human Agentl To determine the current state of interacting humans from

observations and from explicit communications with those humans

l The Human Agent’s assessment of how to interact with a local human is also passed on to the Self Agent, where it can


human is also passed on to the Self Agent, where it can influence deliberations concerning the robot’s own intentions.

l The Self Agent then interprets this suggestion in the context of its own current state, e.g. current intention, status, tasks, etc.

l This processing guides the robot to choose socially appropriate behaviors, leading toward an ultimate goal of completing tasks with (or for) people.

2.5 The Self Agent

n The Self Agent: a cognitive agentl For monitoring sensor signals, agent communications, and

high-level (i.e. cognitive) decision-making aspects of ISAC.

l Integrates failure information from sensors and maintains information about the task-level status of the humanoid.


l The cognitive aspects: include recognition of the human intention from the Human Agent and coordination of appropriate actions by activating meta-level behaviors within the LTM.

2.5 The Self Agent

Emotion Agent


Figure 2.3 Structure /Functions of Self Agent in IMA

2.5 The Self Agent

n The Central Executive Controller (CEC)l To coordinate the various Procedural Memory structures stored

in the Long Term Memory that encapsulate ISAC's behaviors. l These behaviors may be invoked as atomic steps in plans

constructed by the CEC to perform various novel tasks.l The goal of each generated plan is determined by input from

the Intention Agent.


the Intention Agent. l Constructed plans are put into action by activating appropriate

atomic agents. l Component actions are molded to the current task situation

through the adjustment of parameters to meta-level behaviors, guided by the descriptive memory residing in the LTM, sensory information received from atomic agents, and input from the SES.

2.5 The Self Agent

n The Intention Agentl To determine the intended action of the humanoid based on

the intention of the human, the state of the humanoid, and whether this action would conflict with the humanoid's current activities.


n The Emotion Agent l Emotional state of the robot describes what the robot feels toward

the task and the environment based on past experience.l Adding an Emotion Agent to the Self Agent to conduct cognitive

control experiments.

2.5 The Self Agent

n Anomaly Detection Agent (ADA): l A reflective process

l monitoring the inputs and outputs of the atomic agents in the system and will gather statistics concerning the distributions of these inputs and outputs, conditioned on the current intention maintained by the Self Agent


Figure 2.4 Anomaly Detection Agent

2.5 The Self Agent

n Mental Experimentation Agent (MEA): l Another reflective processl When an impasse is raised, it will be initially assumed that

the fault involves the selection of an improper action sequence. Thus, the MEA initially invokes the Central Executive Controller to produce an action sequence appropriate for current task conditions and the current intention.


2.5 The Self Agent

n If both the planning mechanism of the Central Executive Controller and the control parameter search of the MEA fail to resolve the impasse, this reflective process will prompt a query to nearby humans, orchestrated by the Human Agent, in hopes that human assistance will result in the removal of the problem.


the problem.

2.5 The Self Agent

n This initial attempt to integrate self-reflective reasoning into ISAC will result in a system capable of increased flexibility through self-diagnosis and self-correction.

n This approach focuses exclusively on the diagnosis of problems that are immediately detected – that


of problems that are immediately detected – that immediately produce an impasse.

n It is likely that some form of episodic memory will have to be provided to ISAC in order to allow it to fully reflect on its own limitations.l It cannot search the sequence of recently performed actions

for the cause of a task failure, because it does not record these actions or even the states through which it passes.

2.5 The Self Agent

n Machine Consciousnessl A software agent called the Self Agent that uses emotion, attention

and cognitive control to model “consciousness” for ISAC.

l Through a set of tightly-coupled atomic agents trying to achieve a common goal of which concept was inspired by Minsky’s work in the Society of Mind


l Phenomenal consciousness, sometimes called sentience, or subjective (first person) experience

l Self-consciousness, that is being aware of oneself. This often includes one’s self image.

2.5 The Self Agent

l Functional consciousnessif its architecture and mechanisms allow it a number of followings and, perhaps, other functions:

- It helps us deal with novel or problematic situations for which we have no automatized response.

- It makes us aware of potentially dangerous situations.


- It alerts us to opportunities presented by the environment.

- It allows us to perform tasks that require knowledge of location, shape, size or other features of objects in our environments.

- And there are a number of other functions of consciousness.

2.5 The Self Agent

n Self Agentl represents the sense of self through monitoring the robot’s

own internal state as well as the progress of task executionvia sensor signals, agent communications and working memory.

l The internal representation of the robot’s self should continually be updated and enhanced to allow the system


continually be updated and enhanced to allow the system to reason and act based on its status and the context of assigned tasks.

l responds to commands given by humans through the Human Agent and is responsible for controlling task execution.

Self Agent


3. MEMORY STRUCTURE


3.1 Introduction

n An adaptive working memory: l For robot control and learning, closely tied to the learning,

execution of tasks, decision making capabilities by focusing on essential task information and discarding distractions

n To integrate the memory structure into a robot to explore the issues of task learning in a physical embodiment. l This leads to a complex but realistic system involving


l This leads to a complex but realistic system involving perceptual systems, actuators, reasoning, and short-term and long-term memory structures.

l inspired by the hypothesized contribution that the prefrontal cortex (PFC) of the human brain makes to working memory and cognitive control.

n Planned experiments intended to evaluate the utility of the adaptive working memory.

3.1 Introduction

n The goal is to support the robot with the followings:l Focus attention on the most relevant features of the current task.

l Support generalization of tasks without explicitly programming the robot.

l Guide perceptual processes by limiting the perceptual search space.


space.

l Provide a focused short-term memory to prevent the robot from being confused by occlusions, i.e., to avoid the out of sight, out of mind, problem.

l Provide robust operation in the presence of distracting irrelevant events.

3.2 MEMORY STRUCTURE

n Short-Term Memory (STM), Long-Term Memory (LTM), and the Working Memory System (WMS).

n The STM holds sensory information about the current environment.

n The LTM holds learned and taught behaviors, semantic knowledge, and past experience.


knowledge, and past experience. n The WMS holds task-specific STM and LTM information and

streamlines the information flow to the cognitive processes during the task

3.2.1 Short-Term Memory: The Sensory EgoSphere

n A sparse sensory data structure, called the Sensory EgoSphere (SES) to hold STM data.

n The SESl Inspired by the egosphere concept, defined by Albus, serves as

a spatio-temporal STM for a robot.Structured as a geodesic sphere centered between the robot's


l Structured as a geodesic sphere centered between the robot's cameras and indexed by azimuth and elevation.

l Each vertex of the geodesic sphere contains a database representing a detected stimulus at the corresponding angle

l Combines a geodesic dome interface with a database to store sensory events that occur in the robot’s environment.

l The manager queries the database of the SES to register data, to retrieve data and to remove old data.

Short Term Memory and Attention

n A definition for visual attention:

Fig. 3.1 SES


n A definition for visual attention:l Attention is the allocation of resources. l It is a mechanism that chooses which part of the image will

get the computational resources.

n An attention network in the SES: l To focus ISAC’s resources on the most significant area of

the environment: - Bottom-up (or stimulus-driven) and Top-down (or goal-directed)

Bottom-up architecture for attention


3.2.2 Long-Term Memory

n LTM including PM, DM, EM, stores information such as skills learned and experiences gained for future retrieval.

n Procedural memory (PM) contains generic behaviors:l Motion primitives and behaviors needed for movement

- An example of PM is “how to reach and grasp an object.”l To generate such behaviors,


- A spatial-temporal Isomap approach- Motion data are collected from teleoperation then segmented into

a set of motion primitives. Then S-T Isomap dimension reduction, clustering and interpolation methods are applied to the motion segments to produce motion primitives and behaviors

- The Verbs and Adverbs method- Another data structure containing declarative memory within the LTM.

Long-Term Memory


Fig. 3.2 Derivation of Procedural Memory through human-guided motion stream.

Long-Term Memory

n Declarative memory (DM):l A data structure about objects in the environment. l Goals and task sequences are stored as DM units.

n Episodic memory (EM):l To store past experience.


l To store past experience.

STM and LTM

n The short-term memory (STM)l uses the Sensory EgoSphere (SES) to represent short-term

external events in the environment. l The SES is a data structure that provides a short-term-memory

to store events, such as the state of external human agents.l To provide an estimate of the current external state for

determining appropriate task-level intentions for the robot.l Based on these intentions, the self-agent uses procedures


l Based on these intentions, the self-agent uses procedures in LTM to provide control commands to accomplish the robot's intentions.

n The long-term memory (LTM)l contains information about procedures considered intrinsic

to the robot. l A self-agent uses derived behaviors as procedures to produce

motion control for achieving robot objectives.

3.2.3 Working Memory

n Working memory:l What allows you to remember the telephone number that

the directory assistance operator just recited, retaining it only long enough to dial it yourself.

l What allows you to stay focused on the search for a specific product on the shelves of a grocery store


a specific product on the shelves of a grocery store after looking up from your shopping list.

l What allows you to keep track of the particular location in which you last had a clear view of a stalking wolf.

Working Memory System

n Closely tied to task learning and executionn Represents a limited-capacity store

l for retaining information over the short term and l for performing mental operations on the contents of

this store.

n To develop a more complex, but realistic robotic


n To develop a more complex, but realistic robotic learning system involving perceptual systems, actuators, reasoning, attention, emotion, and short-and long-term memory structures.

n The backboard of the mindn Includes the Central Executive Agent and short- and

long-term working memories.

The Role of Working Memory

n Short-term memoryl For visual-spatial information and for speech-based information

(Baddeley 1986).

n Long-term memoryl To include somewhat segregated systems for semantic

knowledge and for specific episodes (Tulving 1972)l Memory in general has been found to have both implicit


Memory in general has been found to have both implicit (i.e., inaccessible to consciousness) and explicit components(Warrington and Weiskrantz 1968).

n Working memory:l The memory that actively maintains transient information that is

critical for successful decision-making in the current context.l A mechanism that protects a small number of informational

“chunks” from interference and distraction and places them in a position to directly influence behavior (Goldman-Rakic ’87).

The Role of Working Memory

l Updated quickly, with its contents dynamically manipulated at sub-second rates, while many longer-term memory systems are much slower to adapt (Waugh and Norman 1965).

l Its contents are seen as readily available to deliberative and executive control processes (Norman and Shallice 1986).

l Its limited capacity constrains the amount of information immediately available to explicit reasoning and executive control processes.


à Its contents must be carefully selected.l Capable of representing and actively maintaining different types

of information, including information about spatial locations, about recently viewed objects, about sought or expected objects, and about rules for action, and also the encoding of verbal forms of information (Demb et al. 1995).

l The activity of prefrontal cells (PFC) specifically capture information that is critical to the performance of the current task (Rainer et al. 88).

Initial Working Memory Efforts

n Its small size and tight coupling with deliberation mechanisms alleviates the need for costly memory searches or retrievals. l Information needed to fluently perform the current task

is temporarily kept “handy” in the working memory store.


n The primary challenge for such a working memory system l Determining whether a given chunk of information should

be maintained in working memory or not.

l Learning when to store a particular chunk of informationin working memory.


n The problem of learning when to update the contents

of working memory

l The PFC receives dense inputs from neurons in the midbrain

that communicate using a neurotransmitter called dopamine

for changes in expected future reward.


l These dopamine neurons are involved in a kind of

reinforcement learning called temporal difference (TD)

learning, since this class of learning algorithms depends

critically on a measure of change in expected future reward.


n Initial efforts towards integrating an adaptable working memory into robotic systems that have the ability to interact physically with their environment.

n Using a model of working memory based on temporal difference learning, as inspired by evidence in


difference learning, as inspired by evidence in cognitive neuroscience, a working memory structure for robotic systems is being implemented.

4. COGNITIVE CONTROL AND THE CENTRAL EXECUTIVE AGENT


4.1 Cognitive Control

n A goal of cognitive robotl To be fluent in routine operations and capable of adjusting

behaviors in the face of unexpected situationsn Cognitive control in humans is the ability to “consciously

manipulate thoughts and behaviors using attention to deal with conflicting goals and demands”

n Cognitive control is thought to be useful for a cognitive robotduring the action selection process


during the action selection processl It guides the robot through the search for component behaviors

that might be combined and used efficiently to execute routine tasks as well as to appropriately respond in novel situations.

n As levels of human behavioral processes range from reactive to full deliberation, cognitive control must be able to switch between these levels to cope with the demand of task and performance, particularly in novel situations.


n Biological evidence of cognitive control in humansl can be found in the function of the basal ganglia

thalamocortical system, the prefrontal cortex (PFC), and the anterior cingulated cortex (ACC).

l Basal ganglia are involved in the planning and execution of complex motor and cognitive acts.


complex motor and cognitive acts.

l The PFC is involved in guiding these actions by supportingrepresentations of relevant information from interference due to competing information.

l The ACC is involved in detecting and helping to resolve response conflicts during a task performance.


n Cognitive control in human is performed through the working memory in the pre-frontal cortex (PFC).

n Furthermore, attention and emotion play an important role in human’s decision and task execution.

n One classical model of working memory by Baddely and Hitch in which the control of executive processes


and Hitch in which the control of executive processes is done by a component called the Central Executive.

n Cognitive control in ISAC is implemented using the Central Executive Agent (CEA) that interfaces with the WMS which allows task related information to be actively maintained during a task execution.

4.2 Central Executive Agent

n CEA’s functions: Task planning, action selection, and action execution

n Goal-oriented behavior selection and execution is being done in a modular fashion. l Upon receiving a command, the CEA associates a set of

behaviors based on past experience and places them in the WMS.


n State estimators produce estimated states to calculate task relevancies of each behavior according to the goal.

n The behavior selector computes time-varying weights wibased on task relevancies to combine behaviors to generate the final action.

n Results from the task execution are used to calculate expected rewards for the TD-Learning.

4.2 Central Executive Agent


Fig. 4.1 Interaction between the CEA and WMS during a task execution

4.3 Enabling Robotic Components

n Central Executive (CE)l For high-level executive functions such as a goal-directed

action selection process called cognitive control.

l The CE and the WM System perform the role of cognitive control.


n Cognitive (or executive) controll “The ability of the brain to identify possible goals and figure out

how to achieve them … and ignore the distractions and impulses that would derail our goal directed efforts” (Miller, 03)



Fig. 4.2 MultiAgent-based Cognitive Robot Architecture


n All behaviors taught or learned are stored in the Long-Term Memory (LTM) (Erol et al. 2003).l A behavior controller and a state estimator are assigned to

each behavior. l When a new command or goal is given, appropriate behaviors

are loaded into the WMS based on TD-Learning.The CE then selects behaviors based on the task relevancy.


l The CE then selects behaviors based on the task relevancy. Task relevancy for a behavior, Bi, is defined as

where the error, ei is defined as the difference between the current state and the estimated state.


n The state estimator is a component of a behavior module that has been loaded into the WMS. l The state estimator predicts the state of the system at time t+1

for its assigned module. l This estimated state is used to calculate task relevancy for the

system to evaluate movements most relevant to the goal.


n The role of the TD-Learning systeml To select the behaviors that will be appropriate for the task at hand. l A new reward is calculated for each behavior module after

attempting the task with that module. l Sets of behaviors that are selected most often when performing

a task and sets of behaviors that complete the task most quickly will be rewarded.

4.4 Spatial Reasoning

n The capability of modeling spatial relations to support innate spatial cognition as well as linguistic, human-robot communication based on this cognition

n Interactive Spatial Languagel To provide capabilities of performing interactive experiments

to test the working memorye.g., “Find the object to the right of the cup.”


e.g., “Find the object to the right of the cup.”l To be used by the robot to describe its environment, such as

“There is a cup on the table to the left of the telephone.”Human: “How many objects do you see?”Robot: “I see 4 objects.”Human: “Where are they located?”Robot: “There are two objects in front of me, and one object on my right.”Human: “The nearest object in front of you is a telephone. Place the cup

to the left of the telephone.”

4.4 Spatial Reasoning

n Spatial Representationsl The position of recognized objects will be stored in a robot-centric

frame called the Sensory Ego Sphere (SES) (Peters et al. 2001).l The SES is a database implementation of Albus’s proposed

egosphere (1991). This spatial database provides an egocentric view of the world which is consistent with the robot’s viewing perspective.

l The SES structure is a geodesic dome with a default frequency


l The SES structure is a geodesic dome with a default frequency of 13, yielding a resolution of about 5 degrees with 1680 hexagonally-connected triangles.

l Each vertex can be labeled with an object identifier; some objects span multiple vertices.

l Objects may be retrieved using azimuth and elevation angles as indices into the database.

l An azimuth and elevation may also define a starting point in a search, for example, to look for an object in a specified region.

5. CURRENT COGNITIVE CONTROL EXPERIMENT



n An integrated cognitive system experiment based on the CEA, attention, emotion and the adaptive working memory system:

1. ISAC is trained to learn specific object using voice, vision, attention. (Learn by association)

2. ISAC is asked to point to one of the learned objects. (Use of short-term memory of the object and long-term procedural


(Use of short-term memory of the object and long-term procedural memory) (Figure 5.1)

3. ISAC is asked to visually track the object held by a human. (Color tracking)

4. A person enters the room and yells "Fire!" ISAC using attention, emotion and cognitive control, suspend the current tracking task and warn everyone to exit the room (Cognitive control).



Fig. 5.1 ISAC is asked to point to one of the learned objects.

Fig. 5.2 Cognitive Control Experiment.


n In Step 4, ISAC's cognitive control must• pay attention to new stimulus and• use emotion to activate cognitive control.

n This experiment is being done through integrating the WM and cognitive control with the existing IMA


the WM and cognitive control with the existing IMA agentsl to demonstrate that "The artificial cognitive machine is not

governed by any programs and therefore will not execute any preprogrammed decision commands like the IF-THEN ones.”

6. CONCLUSIONS

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

n This lecture has summarized the challenge for the realization of a cognitive robot using cognitive control, attention, emotion, and an adaptive working memory system, where multiagent-


and an adaptive working memory system, where multiagent-based cognitive approach is an attempt to capture brain-style computation without necessarily committing to the neural-level details.

REFERENCES

6.1 A Multi-Agent Approach to Self-Reflection for Cognitive Robotics

6.2 A Biologically Inspired Adaptive Working Memory for Robots

6.3 The Sensory Ego-Sphere as a Short-Term Memory for Humanoids


6.4 Modular Behavior Control for a Cognitive Robot

6.5 Development of a Robot with a Sense of Self

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