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From Intelligent Control to Cognitive Control: A Perspective from Cognitive Robot Engineering Point of View Kaz Kawamura Center for Intelligent Systems.

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Presentation on theme: "From Intelligent Control to Cognitive Control: A Perspective from Cognitive Robot Engineering Point of View Kaz Kawamura Center for Intelligent Systems."— Presentation transcript:

1 From Intelligent Control to Cognitive Control: A Perspective from Cognitive Robot Engineering Point of View Kaz Kawamura Center for Intelligent Systems Vanderbilt University

2 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.

3 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.

4 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).

5 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.

6 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.)

7 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.

8 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)

9 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 brains executive, in Fundamentals of the Brain and Mind, Lecture 8, June 11-13, 2003, MIT. Cognitive Control Vanderbilt University

10 Stroop Test (Experiment) (J.R. Stroop, Studies of interference in serial verbal reaction, J. of Experimental Psychology, 1935) One classic experiment used to demonstrate and test the ability of human cognitive control to inhibit competing responses. The participants are asked to do the following task: In this task, a lists of words is presented on colored papers. name the color of the written text (red, blue, green, etc.) as fast as possible. Experiments found that people are more easily distracted by words than color, i.e. they had a harder time to name the color different from the text written on it. Cogniti ve Control

11 NASA-JSC Robonaut Demo:An Ultimate Cognitive Robot?

12 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 OReagan) Ability to use attention and emotion to control behaviors (cognitive control) NASAs Robonaut Vanderbilt University

13 Concept of a Cognitive Robotic System Adapted from a DARPA ITPO Program web site, 2003.

14 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.

15 Action Stimuli Actuators Sensors Behavior 1…Behavior N … Behaviors Legend SES= Sensory EgoSphere PM= Procedural Memory SM=Semantic Memory EM=Episodic Memory CEA=Central Executive Agent STM Attention Network SES SM EM LTM PM Self Agent CEA Human Agent Atomic Agents Perception Encodings Head Agent Hand Agents Arm Agents Working Memory System Completed Currently being implemented

16 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. Vanderbilt University

17 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

18 1. Control Structure used during working memory system training

19 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). Vanderbilt University

20 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 Vanderbilt University

21 Working Memory System Training

22 Experiment I (contd) Sample configuration for reaching (top view) Second sample configuration (top view) Vanderbilt University

23 Experiment I - Video Vanderbilt University

24 Learning Results for Reaching Action

25 Experiment II: Situation-Based Task Switching (Under Investigation) Vanderbilt University

26 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

27 Situation-based Action Selection (Under investigation)

28 Experiment II - Video Vanderbilt University

29 Simulation Results

30 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)

31 For further information, please visit our website at: Vanderbilt University

32 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.

33 Human Agent

34 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.

35 Behavior 1…Behavior N … Behaviors SES SM EM PM Self Agent STM LTM Human Agent Legend SES= Sensory EgoSphere PM= Procedural Memory SM=Semantic Memory EM=Episodic Memory CEA=Central Executive Agent Central Executive Agent Description Agent Anomaly Detection Agent Mental Experiment Agent Intention Agent Activator Agent Emotion Agent Atomic Agents First-order Response Agent Completed Currently being implemented Not yet implemented Working Memory System

36 Central Executive Agent (CEA): Robotic Frontal Lobes responsible for cognitive control functions Inspired by the central executive from Baddeleys 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, Decision Making Task Execution Task-related Percepts Response To Percepts From Initial Knowledge From Environment Task execution sequences Candidate Task Execution Sequences Selected Task Execution Sequences Action Feedback Vanderbilt University

37 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? Vanderbilt University


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