1
TOWARDS COGNITION IN ROBOTICS:
ACHIEVEMENTS AND CHALLENGES
Fakhri KarrayPattern Analysis Machine Intelligence
Research LabUniversity of Waterloo, Canada
http://pami.uwaterloo.ca
AIS’10, Povoa de Varzim, Portugal
Adam is first robot to ever complete a reasoning cycle from hypothesis to experiment to reformulating the hypothesis without human intervention
(One of Time’s Top 10 Discoveries of 2009)
3
IEEE Spectrum Online March 12, 2009 (reporting on Boston Globe article of March 4, 2009)
4
5
Outline
Part1 : Towards Cognition in Robotics
Part2 : Major Aspects of Cognitive Robotics
Part3 : Recent Advances
Part4 : Applications
Part5 : Opportunities /Challenges
6
Toward Cognition in Robotics
Part2 : Major Aspects of Cognitive RoboticsPart3 : Recent AdvancesPart4 : ApplicationsPart5 : Challenges
Part 1:
7
Evolutionary Stages
IndustrialRobotics
Service Robots forPersonal Use
Personal/socialRobotics
ServiceRobotics
Evolution of Robotics What is Cognition?
Service Robots for Professional Use
Toward Cognition in Robotics
8
The need For Cognition
to learn from experience and apply the learned knowledge to deal with dynamically changing environments
to ‘understand’ human and naturally communicate with them
to reason and self-reflect to take new task initiatives
We expect next generation of robots to have the following capabilities :
Evolution of Robotics What is Cognition?
Toward Cognition in Robotics
9
These skills emerge in human as a result of complex, bidirectional interaction among body, brain, and environment
Self-awareness
Perception
Learning
Cognitive robots are robots imparted with ‘human-like’ cognitive abilities
Major Properties in Human Cognition
Evolution of Robotics What is Cognition?
Knowledge
Reasoning
Planning and decision making
Toward Cognition in Robotics
10
Psychology
Neuro-science
Biology
Cognition
Understanding of human cognition involves three major disciplines
Toward Cognition in RoboticsEvolution of Robotics What is Cognition?
11
Understanding of cognitive robotics involves :
Psychology
Neuro-science
Biology
Computer/Software Engineering
Mechanical/ Mechatronics Engineering
AI/ Computer Science
Systems Engineering
Toward Cognition in RoboticsEvolution of Robotics What is Cognition?
Cognitive Robotics
12
Cognitive Robotics
Toward Cognition in RoboticsEvolution of Robotics What is Cognition?
Processing Units
Machines Units
1
1
10s
100s
100s10s
SI
DAI
AI Robotics Centralized Control
Multiple MachinesMachine
MultiagentDistributed
Robot System
SwarmRobotics
MEMS-based Multiple Machines
13
Major properties required for cognition in Robotics
Physical embodiment
Social situatedness
Role of experience
Self-awareness
Value system
Toward Cognition in RoboticsEvolution of Robotics What is Cognition?
14
Cognition Pathways
Perception pathway
Reasoning pathway
Planning pathway
Action pathway
Sens
ors
Actu
ator
s
Mem
ory
Evolution of Robotics What is Cognition?
Toward Cognition in Robotics
15
Major Aspects of Cognitive Robotics
Part3 : Recent AdvancesPart4 : ApplicationsPart5 : Challenges
Part1 : Toward Cognition in Robotics
Part2 :
16
Major Aspects of Cognitive Robotics
The major aspects in cognitive robotics research deal with
Various proposed theories of cognition/architectures
Essential building blocks for achieving cognitive systems
17
Major Aspects of Cognitive Robotics
The research on robot cognition is still in its infancy. A complete and unified theory for robot cognition is not yet developed
Group 1 Theory of Robot cognition
Group 2 Discrete Cognitive Abilities
Main lines of current research
Components TheoriesLine of research
18
Major Aspects of Cognitive Robotics
Group 1 General theories for robot cognition
Design Principle Architectural issues Cognitive developmental robotics Incremental intelligence
Implementation Issues Developmental engineering Software/hardware requirements
Social factors Human/Machine interaction Autonomous learning development
Components TheoriesLine of research
19
Major Aspects of Cognitive Robotics
Group 2 : Discrete cognitive skills
Visual attention Theoretical models Robotic models Task-specific models
Social cognition Joint attention Social imitation
Value system
Components TheoriesLine of research
20
Computational perception
Knowledge Representation
Learning
Reasoning and planning
Interaction and communication
The major building blocks of Cognitive Robots involve the following components :
Components Theories
Major Aspects of Cognitive RoboticsLine of research
21
2- Knowledge Representation
1- Computational perception
3-Learning4-Reasoning and planning
5- Interaction and
communication
Major Aspects of Cognitive RoboticsComponents TheoriesLine of research
Perception in cognitive robotics : allows robot to receive multi-sensory input from external
environment
is crucial in realizing self-awareness capability
is connected to all other components
Attention is the main mechanism required in this process
22
Knowledge representation: allows ease of information storage and retrieval
performs inference to obtain new information from learned data
The robot deploys mechanisms such as self-organization, self-production, and self-maintenance to autonomously develop its own representation with interaction and experience.
2- Knowledge Representation
1- Computational perception
3-Learning4-Reasoning and planning
5- Interaction and
communication
Major Aspects of Cognitive RoboticsComponents TheoriesLine of research
23
Construct new skills, knowledge and capabilities through: Exploration: e.g. reaching, grasping, and manipulating what is
around it.
Social Interaction: which involves interacting with the user in the learning process.
Observation and Imitation: can be imitation of low level features such as joint trajectories, or higher level features such us complete actions, and behaviors.
2- Knowledge Representation
1- Computational perception
3-Learning4-Reasoning and planning
5- Interaction and
communication
Major Aspects of Cognitive RoboticsComponents TheoriesLine of research
24
Consists of a set of three simultaneous processes: Reactive processes: they mimic the reflexive behavior observed in
biological systems.
Deliberative processes: they are in charge of realizing what is commonly known as thinking in biological systems (e.g. Motion planning).
Reflective planning: they involve high-level reasoning where robot computes how to perform a given task based on its cognitive abilities, learned knowledge, and embodiments.
2- Knowledge Representation
1- Computational perception
3-Learning4-Reasoning and planning
5- Interaction and
communication
Major Aspects of Cognitive RoboticsComponents TheoriesLine of research
25
2- Knowledge Representation
1- Computational perception
3-Learning4-Reasoning and planning
5- Interaction and
communication
Major Aspects of Cognitive Robotics
The human role and the level of interaction will vary but the human is still part of the system.
CooperaTIoN
Components TheoriesLine of research
26
Multimodality
Adaptivity
Multimodal Interaction
MultimodalModules
Social Interaction
Multimodal&
AdpativityModules
Adaptive Interaction
AdpativityModules
Direct Manipulation Interaction
WIMP-basedGUIs
S R
/R I
S R/R I: Service Request/Response Interface
S R
/R I
S R
/R I
S R
/R I
S R
/R I
S R
/R I
S R
/R I
S R
/R I
Alaa Khamis and Mohamed Kamel, and Miguel A. Salichs, “Human-Robot Interfaces for Social Interaction”, International Journal of Robotics and Automation, vol. 22, (206), 2007.
2- Knowledge Representation
1- Computational perception
3-Learning4-Reasoning and planning
5- Interaction and
communication
Major Aspects of Cognitive RoboticsComponents TheoriesLine of research
27
Major Theories on Cognitive Robotics
Major Aspects of Cognitive RoboticsComponents TheoriesLine of research
D e v e l o p m e n t i n r o b o t c o g n i t i on
1990 20052000
Cognitive Developmental RoboticsAsada 2001, 2007, 2009
Autonomous Mental DevelopmentWeng 2000
Conscious MachineKawamura 2005
Incremental IntelligenceBrooks 1991
Theory of developmentPiaget 1953
1950
Brain Based DevicesKrichmar 1998
Developmental EngineeringSandini 1997
2007
Confabulation TheoryHecht-Nielsen 2005, 2007
2009
28
Incremental Intelligence
Developmental Engineering
Brain-based Device
Autonomous Mental Development
Cognitive Developmental Robotics
First introduced by Brooks, 1991.
The main implications of this theory are requirement for :
developmental (incremental) nature of intelligence,
necessity of social situatedness and communication,
and robot capability to integrate multi-modality sensory data.
Major Aspects of Cognitive RoboticsComponents TheoriesLine of research
Conscious Machine
29
First introduced by Sandini, et al. 1997.
DE has emphasis on four basic principles:
physical embodiment,
social interaction,
development and experience, and
dynamic of development.
Major Aspects of Cognitive Robotics
Incremental Intelligence
Developmental Engineering
Brain-based Device
Autonomous Mental Development
Cognitive Developmental Robotics
Components TheoriesLine of research
Conscious Machine
30
First introduced by Krichmar et al., 2003 as a platform to test computational models of nervous systems.
BBD devices use authentic simulation of the primate nervous systems to realize their cognitive abilities such as reasoning and planning.
Major Aspects of Cognitive Robotics
Incremental Intelligence
Developmental Engineering
Brain-based Device
Autonomous Mental Development
Cognitive Developmental Robotics
Components TheoriesLine of research
Conscious Machine
31
Was introduced by Weng et al., 2000
AMD requires the cognitive robot to be designed in a task non-specific manner assuming that robot is capable of developing the task specification program by itself.
The main principles of AMD are
embodiment,
self-awareness,
self effectiveness,
and developmental program.
Major Aspects of Cognitive Robotics
Incremental Intelligence
Developmental Engineering
Brain-based Device
Autonomous Mental Development
Cognitive Developmental Robotics
Components TheoriesLine of research
Conscious Machine
32
Was introduced by Asada et al., 2001
CDR argues for the existence of a built-in architecture and puts emphasis on the role of social interaction on autonomous development of representation.
CDR focuses on three main issues, embodiment in concert with Books theory, embedded structure (built-in architecture), and interaction design.
Major Aspects of Cognitive Robotics
Incremental Intelligence
Developmental Engineering
Brain-based Device
Autonomous Mental Development
Cognitive Developmental Robotics
Components TheoriesLine of research
Conscious Machine
33
Incremental Intelligence
Developmental Engineering
Brain-based Device
Autonomous Mental Development
Cognitive Developmental Robotics
Conscious Machine
Was introduced by Kawamura et al., 2005
CM essentially provides a method of implementation for cognitive skills inspired by findings in cognitive psychology.
Authors of CM restrict their work into emotion and attention-based control of behavior in designing consciousness.
CM takes a multi-agent and behavior based methodology with each (software) agent dedicated to a specific cognitive ability and maintains coordination among these agents using a built-in central architecture called intelligent machine architecture.
Part 3: Aspects, Components and Architectures of Cognitive RoboticsComponents TheoriesLine of research
34
Recent Advances
Part4 : ApplicationsPart5 : Challenges
Part1 : Toward Cognition in RoboticsPart2 : Major Aspects of Cognitive Robotics
Part3 :
35
Design of Confabulation Architecture
Novel design of discrete cognitive skills
Intelligent Human Machine Interaction
Besides the theories proposed, some recent advances in Cognitive robotics were made in :
Recent Advances
36
Confabulation Theory First development made by Hecht-Nielsen in 2004-2005
claimed to be the first comprehensive theory to describe cognition in humans and animals.
postulates that cognition constitutes four fundamental elements: Mental object representation: about 4000 thalamocortical modules
each comprising 10,000 to 100,000s of symbols Knowledge links: unidirectional association between two symbols Confabulation: the symbol with maximum total input excitation is
activated Action command origination: confabulation conclusion launches a
set of pre-associated action commands
Recent AdvancesConfabulation Architecture Discreet cognitive skills
37
Confabulation Theory provides a hypothesis and neuronal model for acquiring, storing and using knowledge
Recent AdvancesConfabulation Architecture Discreet cognitive skills
An apple object and some of the attributesKnowledge link linking word apple to color
38
Recent Advances
Design of Discrete Cognitive Skills
Visual attention
Social cognition
Joint attention
Social imitation
Multimodal Interaction/Multimodal attention
Value system
Confabulation Architecture Discrete cognitive skills
39
Recent Advances
Design of Discrete Cognitive SkillsVisual Attention
Visual attention is a fundamental building block of cognitive
development.
It allows robots to focus their processing power only on the
behaviorally relevant information and therefore facilitates their
interaction with humans and surrounding environment
Confabulation Architecture Discrete cognitive skills
40
Recent Advances
Design of Discrete Cognitive SkillsVisual Attention (Recent works)
Recent works involved a bioinspired probabilistic model of visual attention of cognitive robots.
Work uses Bayesian analysis to recursively estimate the orientation of the camera head of a robot such as a visually salient/behaviorally relevant stimuli resides at the center of the visual field
Observe the postulates of Biased Competitive hypothesis (a famous neurodynamic theory of the primates visual attention) for state transition
Confabulation Architecture Discrete cognitive skills
Visual Attention Model for robotic systems
41
Recursively estimate the head pose which enables a robot to focus on the most salient or behaviorally relevant object in the environment.
The Proposed Model
42
Recent Advances
Design of Discrete Cognitive SkillsProposed Model
Confabulation Architecture Discrete cognitive skills
Functional description of the visual attention model
Visual Attention Model for robotic systems
43
Bottom-up Competition model: Assigns high probability for head poses which let the robot to focus on the visually salient objects.
Top-down Modulation model: Modulates the probability assigned by the Bottom-up competition model based on relevance of different objects with the current behavioral requirement of the robot.
The Proposed Model
Visual Attention Model for robotic systems
44
Implementation of the proposed model for exploration and search
Keynote1.wmv
Visual Attention Model for robotic systems
45
We propose to introduce an additional modality, natural speech, and occasional interaction with human. The speech input from human partner modulates the behavioral requirement of the robot in a top-down manner. Thus the sensor measurement become multi-modal.
The Proposed Model in MultiModal
Visual Attention Model for robotic systems
46
Flow of information in case of auditory modulation of visual attention
Visual Attention Model for robotic systems
47
Implementation of the proposed model with multimodal sensory measurements
49
Recent Advances
Design of Discrete Cognitive SkillsSocial Cognition
Multimodal interaction: allow users to move seamlessly between different modes of interaction, from visual to voice to touch, according to changes in context or user preference.
Method facilitating social interaction between robots and humans, divided into three categories of research :
Confabulation Architecture Discrete cognitive skills
Joint attention: having this ability enables robot to attend to an object of mutual interest intentionally.
Social Imitation: powerful methods of learning and developing social behavior for cognitive robots.
50
Example task in hand: any particular arm motion of the human user
Toward Cognition in RoboticsConfabulation Architecture Discrete cognitive skills
51
Recent Advances
Design of Discrete Cognitive SkillsSocial Cognition – Joint Attention
Confabulation Architecture Discrete cognitive skills
52
Recent Advances
Design of Discrete Cognitive SkillsSocial Cognition – Social Imitation
Confabulation Architecture Discrete cognitive skills
53
Recent Advances
Toward a Comprehensive DataFusion Architecture
Design of Discrete Cognitive SkillsConfabulation Architecture Discrete cognitive skills
a case study: multimodal Interaction
54
Recent Advances
Design of Discrete Cognitive SkillsValue System
Robot’s capability to plan action perception of salient stimuli, which makes it an essential requirement of developing human-like intelligence in robotic systems, mostly through motivation
PredictionTask non specificDevelopmentalValue based learning
Characteristics:
Confabulation Architecture Discrete cognitive skills
55
Recent Advances
Design of Discrete Cognitive SkillsValue System
Confabulation Architecture Discrete cognitive skills
56
Towards a Generalized Performance Metric for HRI
Emergence of new cognitive based systems which require high level of interaction and communication between humans and robots.
Lack of a generalized set of metrics that can span much of the HRI application space.
Lack of empirical and mathematical representation models for human-machine team performance assessment.
Motivation
Shortcomings of Current Performance Metrics
Tend to focus on one agent and ignore the capabilities of the other.
Are usually very task specific and don’t translate well to other applications.
May not give insights on how the task is being accomplished.
May not be extendable to multi-robots scenarios.
57
Towards a Generalized Performance Metric for HRI
Developing a common toolkit has been discussed by Olsen and Goodrich, who proposed six interrelated metrics, most important of which are:
Robot Attention Demand (RAD) is a measure of how much time of total task time must be spent by the user interacting with the robot.
Fan-out (FO), which is a measure of many robots with similar capabilities the user can operate simultaneously and effectively.
Common Toolkit for HMI
Drawbacks
Two essential human related criteria are ignored: Human trust in automation, and human reliability.
Metrics do not generalize to accommodate multi-robot scenarios.
58
Towards a Generalized Performance Metric for HRI
FO is augmented to include a representative index for human reliability.
RAD is divided as: direct interaction time (DIT), and indirect interaction time (IIT)
Where NT is the time in which the robot is being ignored.
Proposed Augmented Metric
Proposed trust Model
Two-level fuzzy temporal model is proposed. Level II is a finite fuzzy state machine that relates trust to some second order perceptions.
Level I infers second order perceptions from first order ones, e.g. fault sized is inferred from fault frequency, cruciality, and recovery.
59
Towards a Generalized Performance Metric for HMI
Three membership functions are used to model first and second order perceptions.
Five states are used to model the human trust in automation factor.
Max-Min rule of inference is used to infer the states activation levels.
Proposed trust model
State activation levels are defuzzified using Sugeno-Like Zero order consequences (0.1, 0.3, 0.5, 0.7, 0.9).
60
Towards a Generalized Performance Metric for HMI
Sequential Scenario: Only one robot is active at a time. Idle robots are assigned zero FO. System FO is calculated as:
Parallel and Task Independent Scenario: Multiple robots are active simultaneously, executing independent tasks.
wi is the percent contribution of the ith robot toward the final goal completion.
Generalization to Multi-robot Scenarios
61
Towards a Generalized Performance Metric for HMI
Parallel and task dependent Scenario: Starting with 2 robots that are active simultaneously, executing independent tasks.
System FO falls between the weighted average and the minimum FO.
Where d is the percent task dependency between the two robots.
Generalizing this results to N-Robots, pair-wise dependencies are considered. An upper bound to practical system FO is calculated as:
Generalization to Multi-robot Scenarios
62
Towards a Generalized Performance Metric for HMI
Some Experimental Results
63
ApplicationsPart5 : Challenges
Part1 : Toward Cognition in RoboticsPart2 : Major Aspects of Cognitive RoboticsPart3 : Recent Advances
Part4 :
64
Applications
Health-care and Assistive robotics
Entertainment robotics
Military and Space exploration
Humanoid/social robotics
65
Applications
Trends in percentage of the elderly (over age 85) in the world
Source: Adriana Tapus, Maha J. Mataric, and Brian Scassellati, “Socially Assistive Robotics: The Grand Challenges in Helping Humans Through Social Interaction,” IEEE Robotics & Automation Magazine, MARCH 2007
Health Care and Assistive Robotics
66
Applications
Domo (MIT robot): A household assistant for the elderly or wheelchair-bound. It grasps objects and places them on shelves or counters.
ASIBOT: A portable assistive robot for elderly and disease people bringing more freedom in daily tasks as eating, drinking, shaving, make up wearing, tooth cleaning, etc.
Huggable: A robotic companion for health care, education, and social communication.
Health Care and Assistive Robotics
67
Applications
Sparky project on AIBO platform: alleviating loneliness and causing to form attachments for nursing home residents.
CiceRobot: A museum guide cognitive robot.
Soccer robot
Flutist robot: The first member of a humanoid robot orchestra.
Entertainment Robotics
Video : Violinist Robot
68
Applications
Robonaut: A robotic system that can function as an EVA astronaut equivalent.
BEAR: Designed to find, pick up and rescue people in harm's way.
Military and Space Exploration
Video : Bear Robot
69
Applications
Maggie: research platform to study human robot interaction and robot intelligence and autonomy.
Humanoid/social robotics
Video : Partner/ Cooperative Dancing with Maggie CB2: child robot with
biomimetic body. It is able to develop a behavior similar to a 1 or 2 years old baby.
PresenterPresentation NotesDuring the CampusPartyTM 2005, Maggie has demonstrated its ability for closely cooperative dancing with humans. In this interaction scenario, Maggie andher partner stay together for the duration of the dance. During this duration, Maggie changes its movements as respond for events detected by tactile sensors as results ofpartner touching.
70
Challenges
Part1 : Toward Cognition in RoboticsPart2 : Major Aspects of Cognitive RoboticsPart3 : Recent AdvancesPart4 : Applications
Part5 :
71
Major progress made in understanding human cognition, but no
complete theory exists yet on how biological system naturally
develop learning functions
Even if a theoretical model is provided, it is not straightfoward to
implement a working system on machines/robots
Need for performance metric to evaluate the cognitive abilities in a
robotic system. Difficulty in providing objective measures
Issue I: Need for Metrics and Evaluation Measure for Cognition
Challenges
72
Need for a cross-disciplinary collaborative work to integrate the
most understood aspects of human cognition in order to come up
with a comprehensive theory for robot cognition.
Lack of comprehensive architecture on which to impart cognitive
skills for robots
Robustness and adaptability of robot cognition from one
environment to the next.
Difficulty in dealing with uncertainty in perception
Issue II: Integration of cross-disciplinary knowledge
Challenges
73
Tighter cooperation involving roboticists, AI specialists, Neuroscientists
and psychologists is much needed
Avoid for now the goal of designing a general theory for robot cognition
and focus on designing robots with cognitive traits that are task specific
More work needed on understanding the dynamics of cognitive paths in
human brains
Further work is needed on the mathematical modeling of developmental
cognition in humans
Final Thoughts
74
References
[Koch 1985] C. Koch and S. Ullman, “Shifts in selective visual attention: toward the underlying neural circuitry," Human Neurobiology, vol. 4, pp. 219 -227, 1985.[Itti 1998] L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis," IEEE Trans. Pattern Analysis and Machine Intelligence., vol. 20, pp. 1254-1259, 1998[Tsotsos 1995] J. K. Tsotsos, S. Culhane, Y. Winky, L. Yuzhong, N. Davis, and F. Nuo, “Modeling visual attention via selective tuning," Artificial Intelligence, vol. 78, pp. 507-545, 1995[Frintrop 2006] S. Frintrop, “VOCUS: A Visual Attention System for Object Detection and Goal-directed Search,” Lecture Notes in Articial Intelligence (LNAI), Vol. 3899, Springer Berlin/Heidelberg. ISBN: 3-540-32759-2, 2006.[Vijayakumar 2001] S. Vijayakumar, J. Conrad, T. Shibata, and S. Schaal, “Overt visual attention for a humanoid robot,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001, pp. 2332 – 2337.[Dankers 2007] A. Dankers, N. Barnes, and A. Zelinsky, “A reactive vision system: Active-dynamic saliency,” in Proceedings of International Conference on Computer Vision Systems, 2007.[Vitay 2005] J. Vitay, N. P. Rougier, and F. Alexandre, A Distributed Model of Spatial Visual Attention. Springer-Verlag, 2005, pp. 54 –72.
75
References
M. Begum, F. Karray, G. K. I. Mann, and R. G. Gosine probabilistic Model of Overt VisualAttention for Cognitive Robots, accepted for publication in IEEE SMC Part B
M. Begum and F. Karray, omputational Intelligence Techniques in Bio-inspired Robotics,Computational Intelligence in Autonomous Robotic Systems, pp. 1-29, Springer 2008
M. Begum, F. Karray, G. K. I. Mann, and R. G. Gosine, Probabilistic Approach forAttention-Based Multi-Modal Human-Robot Interaction, IEEE International Symposiumon Robot and Human Interactive Communication (IEEE RO-MAN), 2009
M. Begum, F. Karray, G. K. I. Mann, and R. G. Gosine, e-mapping of Visual Saliency in Overt Attention: A Particle Filter Approach for Robotic Systems, IEEE International Conference on Robotics and Bio-mimetic, pp. 425-430, 2008
M. Begum, G. K. Mann, R. Gosine, and F. Karray, Object- and space-based visual attention: An Integrated Framework for Autonomous Robots, IEEE/RSJ Intl. Conference on Intelligent Robots and Systems, pp. 301-306, 2008
M. Begum, G. K. I. Mann, and R. G. Gosine, Biologically inspired Bayesian model of vsual attention for humanoid robots, Proceedings of IEEE-RAS International Conference on Humanoid Robots, pp. 587-592, 2006
76
References
[Orabona 2005] F. Orabona, G. Metta, and G. Sandini, “Object-based visualattention: A model for a behaving robot,” in IEEE Conference on Computer Vision and Pattern Recognition, 2005.[Metta 2001] G. Metta, “An attentional system for humanoid robot exploitingspace variant vision,” in IEEE-RAS International Conference onHumanoid Robots, 2001.[Ruesch 2008] J. Ruesch, M. Lopes, A. Bernardino, J. Hornstein, J. S. Victor, and R. Pfeifer, “Multi modal saliency-based bottom-up attention: A framework for the humanoid robot icub,” in IEEE International conference on Robotics and automation, 2008, pp. 962 – 967.[Fleming 2006] K. A. Fleming and R. E. B. R. A. Peter II, “Image mapping and visual attention on a sensory ego-sphere,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006, pp. 241 – 246.[Aryananda 2006] L. Aryananda, “Attending to learn and learning to attend for a social robot,” in IEEE International conference on Humanoid robots, 2006, pp. 618 – 623.[Canas 2008] J. M. Canas, M. M. Casa, and T. Gonzalez, “An overt visual attention mechanism based on saliency dynamics,” International Journal of Intelligent Computing in Medical Sciences and Image Processing, vol. 2, pp. 93 – 100, 2008.[Crespo 2009] J. L. Crespo, A. Faina, and R. J. Duro, “An adaptive detection/ attention mechanism for real time robot operation,” Neurocomputing, vol. 72, pp. 850 – 860, 2009.
Acknowledgements
Dr. Alaa Khamis (PAMI)
Dr. Momotaz Begum (PAMI)
Dr. Malek Baklouti (PAMI, Thales)
Mr. Jamil Abu Saleh (PAMI)
Mr. jeremy Sun (PAMI)
Mr. Ahmed ElMougy (PAMI)
77
78
Campus
University of Waterloo
79
Thank you
Slide Number 1Slide Number 2Slide Number 3Slide Number 4OutlineSlide Number 6Toward Cognition in RoboticsToward Cognition in RoboticsToward Cognition in RoboticsToward Cognition in RoboticsToward Cognition in RoboticsToward Cognition in RoboticsToward Cognition in RoboticsToward Cognition in RoboticsSlide Number 15Major Aspects of Cognitive RoboticsMajor Aspects of Cognitive RoboticsMajor Aspects of Cognitive RoboticsMajor Aspects of Cognitive RoboticsMajor Aspects of Cognitive RoboticsMajor Aspects of Cognitive RoboticsMajor Aspects of Cognitive RoboticsMajor Aspects of Cognitive RoboticsMajor Aspects of Cognitive RoboticsMajor Aspects of Cognitive RoboticsMajor Aspects of Cognitive RoboticsMajor Aspects of Cognitive RoboticsMajor Aspects of Cognitive RoboticsMajor Aspects of Cognitive RoboticsMajor Aspects of Cognitive RoboticsMajor Aspects of Cognitive RoboticsMajor Aspects of Cognitive RoboticsPart 3: Aspects, Components and Architectures of Cognitive RoboticsSlide Number 34Recent AdvancesRecent AdvancesRecent AdvancesRecent AdvancesRecent AdvancesRecent AdvancesVisual Attention Model for robotic systemsRecent AdvancesVisual Attention Model for robotic systemsVisual Attention Model for robotic systemsVisual Attention Model for robotic systemsVisual Attention Model for robotic systemsVisual Attention Model for robotic systemsSlide Number 48Recent AdvancesToward Cognition in RoboticsRecent AdvancesRecent AdvancesRecent AdvancesRecent AdvancesRecent AdvancesTowards a Generalized Performance Metric for HRITowards a Generalized Performance Metric for HRITowards a Generalized Performance Metric for HRITowards a Generalized Performance Metric for HMITowards a Generalized Performance Metric for HMITowards a Generalized Performance Metric for HMITowards a Generalized Performance Metric for HMISlide Number 63ApplicationsApplicationsApplicationsApplicationsApplicationsApplicationsSlide Number 70ChallengesChallengesFinal ThoughtsReferencesReferencesReferencesAcknowledgementsUniversity of WaterlooThank you
Top Related