1. Using Computational Cognitive Models for Better Human-Robot Collaboration
Alan C. Schultz
J. Gregory Trafton
Nick Cassimatis
Navy Center for Applied Research in Artificial Intelligence
Naval Research Laboratory

2. Peer-to-peer collaboration in Human-Robot Teams
Not interested in general, unified grand theory of cognition for solving the whole problem
We already know how to be mobile, avoid collisions,etc
Approach: to be informed from cognitive psychology
Study human-human collaboration
Determine important high-level cognitive skills
Build computational cognitive models of these skills
ACT-R, SOAR, EPIC, Polyscheme…
Use computational models as reasoning mechanism on robot for high-level cognition

3. Cognitive Science as Enabler Cognitive Robotics
Hypothesis:
A system using human-like representations and processes will enable better collaboration with people than a computational system that does not
Similar representations and reasoning mechanisms make it easier for humans to work with the system; more compatible
For close collaboration, systems should act “naturally”
i.e. not do something or say something in a way that detracts from the interaction/collaboration with the human
Robot should accommodate humans; not other way around
Solving tasks from “first principles”
Humans are good at solving some tasks; let’s leverage human’s ability

4. Cognitive Skills Appropriate knowledge representations Problem solving
Spatial representation for spatial reasoning
Adapting representation to problem solving method
Problem solving
Navigation routing with constraints (e.g., remaining hidden)
Learning
Learning to recognize and anticipate others’ behaviors
Learning characteristics of other’s capabilities
Vision
Object permanence and tracking (Cassimatis et al., 04)
Recognizing gestures
Natural language/gestures (Perzanowski et al., 01)

5. Cognitive Skills Perspective-Taking Spatial reasoning
Spatial (Trafton et al., 2005)
Social (Breazeal et al., 2006)
Spatial reasoning
People use metric information implicitly; use and think qualitatively much more frequently (Trafton et al., 2006)
Spatial referencing/language (Skubic et al., 04)
Temporal reasoning
Predicting how long something will take
Anticipation
What does a person need and why?

6. Hide and Seek (Trafton & Schultz, 2004, 2006)
Lots of knowledge about space required
A “good” hider needs visual, spatial perspective taking to find the good hiding places (large amount of spatial knowledge needed)

7. Development of Perspective-Taking
Children start developing (very very basic) perspective-taking ability around age 3-4
Huttenlocher & Presson, 1979; Newcombe & Huttenlocher, 1992; Wallace, Alan, & Tribol, 2001
In general, 3-4 year old children do not have a particularly well developed sense of perspective taking

8. Case Study: Hide and Seek Age 3½
Elena did not have perspective taking ability
Left/right errors
play hide and seek by learning pertinent qualitative features of objects
construct knowledge about hiding that is object-specific

9. Hide and Seek Cognitive Model
Created cognitive model of Elena learning to play hide and seek using ACT-R (Anderson, et al 93, 95, 98, 05)
Correctly models Elena’s behavior at 3½ years of age
Learns and refines hiding behavior based on interactions with “teacher”
Learns production strength based on success and failure of hiding behavior
Learns ontological or schematic knowledge about hiding
Its bad to hide behind something that’s clear
Its good to hide behind something that is big enough
Knows about location of objects (relative) (behind, in front of) adds knowledge about relationships. Model only has syntactic notion of spatial relationships

10. Hybrid Cognitive/Reactive Architecture Robot Hide and Seek
Computational cognitive model of hiding makes deliberative (high-level cognitive) decisions. Models learning.
Using cognitive model of hiding (after learning) in order to reason about what makes a good hiding place in order to seek.
Reactive layer of hybrid model for mobility and sensor processing

11. How important is perspective taking? (Trafton et al., 2005)
Analyzed a corpus of NASA training tapes
Space Station Mission 9A
Two astronauts working in full suits in neutral-buoyancy facility. Third, remote person participates.
Standard protocol analysis techniques; transcribed 8 hours of utterances and gestures (~4000 instances)
Use of spatial language (up, down, forward, in between, my left, etc) and commands
Research questions:
What frames of reference are used?
How often do people switch frames of reference?
How often do people take another person’s perspective?

12. Spatial language in space Results
Frame of Reference
Example
% Utterances
Exocentric
Go straight zenith (“up”) 7%
Egocentric
Turn to my left
15%
Addressee-Centered
Turn to your left
10%
Deictic
Put it over there [Points] 5%
Object-centered
Put it on top of the box
63%
How frequently do people switch their frame of reference?
45% of the time (Consistent with Franklin, Tversky, & Coon, 1992)
How often do people take other people’s perspective (or force others to take theirs)?
25% of the time

13. Perspective Taking and Changing Frames of Reference

14. Perspective Taking and Changing Frames of Reference
Bob, if you come straight down from where you are, uh, and uh kind of peek down under the rail on the nadir side, by your right hand, almost straight nadir, you should see the uh…
Notice the mixing of perspectives: exocentric (down), object-centered (down under the rail), addressee-centered (right hand), and exocentric again (nadir) all in one instruction!
Notice the “new” term developed collaboratively: mystery hand rail

15. “Please hand me the wrench”
Perspective Taking
Perspective taking is critical for collaboration.
How do we model it? (ACT-R, Polyscheme…)
I’ll show several demos that show our current progress on spatial perspective taking
But first a scenario:
“Please hand me the wrench”

16. Perspective taking in human interactions
How do people usually resolve ambiguous references that involve different spatial perspectives? (Clark, 96)
Principle of least effort (which implies least joint effort)
All things being equal, agents try to minimize their effort
Principle of joint salience
The ideal solution to a coordination problem among two or more agents is the solution that is the most salient, prominent, or conspicuous with respect to their current common ground.
In less simple contexts, agents may have to work harder to resolve ambiguous references

17. Perspective Taking: A tale of two systems
ACT-R/S (Schunn & Harrison, 2001)
Our perspective-taking system using ACT-R/S is described in Hiatt et al. 2003
Three Integrated VisuoSpatial buffers
Focal: Object ID; non-metric geon parts
Manipulative: grasping/tracking; metric geons
Configural: navigation; bounding boxes
Polyscheme (Cassimatis)
Computational Cognitive Architecture where:
Mental Simulation is the primitive
Many AI methods are integrated
Our perspective-taking using Polyscheme is described in Trafton et al., 2005

18. Robot Perspective Taking
Human can see one cone Robot can sense two cones
(Fong et al., 06)
give info at beginning of what system/robot is doing

19. Summary
Having similar or compatible representation and reasoning as a human facilitates human-robot collaboration
We’ve developed computational cognitive models of high-level human cognitive skills as reasoning mechanisms for robots
Open questions:
Scale up; combining many such skills
What are the important skills?
Which skills are built upon others?

20. Shameless Advertisement
ACM/IEEE Second International Conference on Human-Robot Interaction
Washington DC, March 9-11, 2007
With HRI 2007 Young Researchers Workshop, March 8, 2007
Single track, highly multi-disciplinary
Robotics, Cognitive Science, HCI, Human factors, Cognitive Psychology…
Submission deadline: August 31, 2006

21. A Dynamic Auditory Scene
Everyday Auditory Scenes are VERY Noisy
Fans
Alarms/Telephones
Traffic
Weather
People

22. Auditory Perspective Taking
Allow a robot to use its knowledge of the environment, both a priori and sensed, to predict what human can hear and effectively understand.
Information Kiosk
Robot uses speech to relay information to an interested human listener.
Given the auditory scene, can the person understand what the robot is saying?
If not, what actions can the robot take to improve intelligibility and knowledge transfer?
Stealth Bot
Robot uses its awareness of the auditory environment to hide from people and or machines.
The robot knows its own acoustic signature
Now predict how each action or location will be heard by the listener, and select the best choice.

23. An Example of Adaptation: Robot Speech Interface
Adjust word usage depending on noise levels
Use smaller words with higher recognition rates.
Ask questions to verify understanding; repeat yourself.
Change the quality of the speech sounds
Adapt voice volume and pitch to overcome local noise levels (Lombard Speech).
Emphasize difficult words.
Don’t talk during loud noises
Reposition Oneself
Vary the proximity to the listener
Face the listener as much as possible
Move to a different location if all else fails.

24. Information Kiosk Overhead Microphone Array Stereo Vision Actions
Tracks local sound levels
Localizes interfering sources
Guides the vision system to new users
Stereo Vision
Tracks the users position in real-time.
Actions
Raise speaking volume relative to users distance and the level of ambient noise
Pause during loud sounds or speech interruptions.
Rotate the robot to face users
Reposition the robot if noise levels become too large.

25. Acoustic Perspective
Noise Maps – Combine Knowledge of Sound Sources to Build Maps
Measured Volume/Frequency Levels
Source Locations/Directionality
Walls and environmental features
Multiple maps can be built and combined in real-time
Modifying action based on noise map
Seeking noisy hiding places so that it can best observe its target without being detected.
masking its particular acoustic signature.
After exploring the area inside the square, 3 air vents are localized by the robot

26. 4 Sources are combined together as omnidirectional sources, without environmental reflections.