Kaz Kawamura Center for Intelligent Systems Vanderbilt University

1. From Intelligent Control to Cognitive Control:APerspective 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 andhaptic-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 andhaptic-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 “cognitivecontrol “ 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. Cognitive Control Vanderbilt University 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.

10. NASA-JSC Robonaut Demo:“An Ultimate Cognitive Robot?”

11. Vanderbilt University 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

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

13. Working Definition • Cognitive Controlfor robotsis the attention- and emotion-based robust sensory-motor intelligence to execute the task in hand or switch tasks under conflicting goals.

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

15. Vanderbilt University 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.

16. 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

17. 1. Control Structure used during working memory system training

18. Vanderbilt University Experiment I: Working Memory Training for a Percept-Action Task • 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).

19. Vanderbilt University Experiment I • ISAC is trained to recognize specific objects ,i.e., several colored bean bags. • ISAC is taught a small set of motion behaviors ,i.e., reach, wave, handshake. • Bean bags are rearranged. • ISAC is asked to “reach to the bean bag” (color is not specified). • ISAC will attempt to load the relevant “chunks” into WMS for appropriate: • action to take (reach, wave, etc.) • percept to act upon. • Over time, ISAC should learn • which “chunk” (i.e., a percept-behavior combination) is the most appropriate to choose

20. Working Memory System Training

21. Second sample configuration (top view) Vanderbilt University Experiment I (cont’d) Sample configuration for reaching (top view)

22. Vanderbilt University Experiment I - Video

23. Learning Results for Reaching Action

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

25. 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 Experiment II

26. Situation-based Action Selection (Under investigation)

27. Vanderbilt University Experiment II - Video

28. Simulation Results

29. 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)

30. Vanderbilt University Thank you! For further information, please visit our website at:http://eecs.vanderbilt.edu/CIS/

31. 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 arobotic aid system using sensor-based intelligent control.

32. Human Agent

33. 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.

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

35. Task Execution Action Selected Task Execution Sequences Decision Making Feedback Candidate Task Execution Sequences Response To Percepts From Environment From Initial Knowledge Task-related Percepts Task execution sequences Vanderbilt University 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.

36. Vanderbilt University 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?