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Human Robot Teams: Concepts, Constraints, and Experiments

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Title: Human Robot Teams: Concepts, Constraints, and Experiments


1
Human Robot TeamsConcepts, Constraints, and
Experiments
Michael A. Goodrich Dan R. Olsen Jr. Brigham
Young University
2
Research Agenda
  • Evaluation Technology
  • Neglect Tolerance
  • Behavioral Entropy
  • Fan-Out
  • Interface Design
  • Mixed Reality Displays
  • Principles
  • HF Experiments
  • Autonomy Design
  • Team-Based Autonomy
  • UAVs
  • Perceptual Learning

3
The Presentation Agenda
  • The types of questions
  • Neglect tolerance Is a team feasible?
  • How do we compute neglect tolerances?
  • Tradeoffs workload and performance
  • Is a team optimal?
  • The problem with switch costs
  • Some limits, ideas, and proposals

4
A Special Case The Robotics Specialist
  • One soldier
  • Two UAVs
  • One UGV
  • Can one person manage all three assets?
  • At what level of performance?
  • At what level of engagement?

5
A More General CaseSpan of Control
  • How many things can be managed by a single
    human?
  • How many robots?
  • How do we measure Span of Control in HRI?
  • Relationships between NT and IT
  • How do we compare possible team configurations?
  • Evaluate performance-workload tradeoffs
  • Identify performance of feasible configurations

6
The Most General Case Multiple Robots
Multiple Humans
  • How many people are responsible for a single
    robot?
  • How many robots can provide information to a
    single human?

Platoon Headquarters Organization
1 CL I UAV System
ARV-A (L)
ICV
1 CL I UAV System
7
The Presentation Agenda
  • The types of questions
  • Neglect tolerance Is a team feasible?
  • How do we compute neglect tolerances?
  • Tradeoffs workload and performance
  • Is a team optimal?
  • The problem with switch costs
  • Some limits, ideas, and proposals

8
Neglect ToleranceNeglect Time and Interaction
Time
  • How long can the robot go without needing human
    input?
  • How long does it take for a human to give
    guidance to the robot?

Neglect Time (NT)
Interaction Time (IT)
9
Fan-Out (Olsen 2003,2004) How many homogeneous
robots?
  • How many interaction periods fit into one
    neglect period
  • Two other robots can be handled while robot 1 is
    neglected
  • Fan-out 3

1
NT
IT
IT
IT
2
3
4
10
Can a human manage team T ? Fan-out and
Feasibility
  • Fan-out (homoeneous teams)
  • Feasibility (heterogeneous teams)
  • These are upper bounds

11
The Presentation Agenda
  • The types of questions
  • Neglect tolerance Is a team feasible?
  • How do we compute neglect tolerances?
  • Tradeoffs workload and performance
  • Is a team optimal?
  • The problem with switch costs
  • Some limits, ideas, and proposals

12
Neglect Impact Curves
  • A task is Neglected if attention is elsewhere
  • Neglect impacts task performance 2ndary tasks

13
Not Neglect Tolerant Enough
14
Too Neglect Tolerant
  • Old Glory Insurance

15
Interface Efficiency Curves
  • Recovery from zero point
  • Imprecise switch costs

16
Efficient Interfaces
  • PDA-based UAV control (versus command line)

17
Efficient Interfaces
  • Phycon-based UAV control (versus command line)

18
Finding NT and IT from the curves
19
Example
  • Vary minimum performance level
  • Measure
  • Average performance
  • Neglect time
  • Interaction time

20
Validation of Method Complexity
  • As complexity goes up, NT goes down and IT goes
    up
  • Feasibility using NT/IT needs more work

21
The Presentation Agenda
  • The types of questions
  • Neglect tolerance Is a team feasible?
  • How do we compute neglect tolerances?
  • Tradeoffs workload and performance
  • Is a team optimal?
  • The problem with switch costs
  • Some limits, ideas, and proposals

22
Existing Tradeoffs
Ideal
23
Types of Autonomy
24
Using Tradeoffs to Select a Configuration
Ideal
Ideal
Ideal
25
Tradeoffs Galore
  • Higher workload means shorter missions
  • Higher performance requires higher workload
  • Higher workload implies smaller span of control
  • Lower risk tolerance implies shorter neglect
    times
  • Longer neglect times imply greater risk of
    unperceived failures
  • Longer neglect times imply more complicated
    other tasks
  • More complicated other tasks imply greater
    switch costs

26
The Presentation Agenda
  • The types of questions
  • Neglect tolerance Is a team feasible?
  • How do we compute neglect tolerances?
  • Tradeoffs workload and performance
  • Is a team optimal?
  • The problem with switch costs
  • Some limits, ideas, and proposals

27
Predicting Performance of a Heterogeneous Team
  • Each robot may have multiple autonomy modes and
    interaction methods
  • Each interaction scheme yields NT, IT, and
    average performance values

28
Predicting Performance continued
29
Using Predicted Performance
  • Eliminate the infeasible (SIT gt NT)
  • Find the maximum performance
  • Use interaction schemes that maximize performance

30
Accuracy of Predictions in a Three-Robot Team
  • Two interaction schemes
  • Point to point (P)
  • Region of Interest (R)
  • Three robots
  • Experiment
  • 23 subjects
  • 148 trials
  • 3 world complexities

31
The Presentation Agenda
  • The types of questions
  • Neglect tolerance Is a team feasible?
  • How do we compute neglect tolerances?
  • Tradeoffs workload and performance
  • Is a team optimal?
  • The problem with switch costs
  • Some limits, ideas, and proposals

32
What are switch costs?
  • The biggest unknown influence on span of control
  • They come in several flavors
  • Time to regain situation awareness
  • Time to prepare for switch
  • Errors and Change Blindness

What really happens here?
33
Before and After
34
Getting a Feel for the Experiment
35
The Experiment
  • Primary task Control a robot
  • Vary type and duration of secondary task
  • Measure speed and accuracy of change detection
  • Measure speed and accuracy of change diagnosis

36
Preliminary Results
  • 6 subjects, none naïve
  • 207 correct change detections
  • One-sided T-test, equal variances

37
Important Trends
  • Differences not just from time away
  • blank and tetris have same time
  • UAV and tone have same time
  • Averages nearly identical
  • Differences not just from counting
  • UAV and tone both count
  • Differences not just from motor channel
  • UAV and tone both select
  • Tetris requires interaction
  • Probably spatial reasoning and changing
    perspectives

38
Summary of Preliminary Results
  • If it takes longer than 20 seconds to diagnose a
    change, the subject has probably failed
  • Need to gather failure rate
  • Averages show very strong trend
  • We conclude
  • The test is sensitive enough to detect
    differences
  • The type of secondary task affects recovery

39
The Presentation Agenda
  • The types of questions
  • Neglect tolerance Is a team feasible?
  • How do we compute neglect tolerances?
  • Tradeoffs workload and performance
  • Is a team optimal?
  • The problem with switch costs
  • Some limits, ideas, and proposals

40
How Many Robots?
  • Assumptions
  • Goal Gather battle-related information while
    minimizing risk
  • Media Mostly camera/video information
  • Prediction
  • Interpreting camera information difficult
  • High robot autonomy wont help enough

41
A Special Case The Robotics Specialist
  • Can one person manage multiple robot assets?
  • At what level of performance?
  • Goal gather information
  • Media visual (camera/video)
  • Belief autonomy will help, but not enough

42
A Research Agenda
  • Phase 1 Refine assessment technique
  • Validate sensitivity
  • Assess feasibility with switch costs
  • Phase 2 Study interfaces
  • Select plausible secondary tasks
  • Compare information presentation techniques
  • Compare PDAs, tablets, workstations
  • Compare interaction while stationary with
    in-motion
  • Phase 3 Study autonomy
  • Vehicle control
  • Team playbooks
  • Interactive perception

43
Pushing the limits
  • Mixed reality displays
  • Let the operator control the camera, not the
    robot
  • robot controls its motion to support camera
  • Highlight changes
  • Mitigate change blindness effects
  • TiVO
  • Support task switching
  • Improve robot perception by teaching it
  • Automate image understanding
  • Effects of false alarms on the human?
  • Costs of missed detections on the mission?
  • 2 to 3, or 3 to 5 ratios redundancy and
    responsibility swapping

44
Mixed Reality Displays
  • Eliminate The world through a soda straw
  • Integrate vision with active sensors
  • Integrate display with autonomy
  • Include sensor uncertainty
  • Control pan-and tilt
  • Study time delay effects

45
Real World Results
  • Objective
  • 51 Faster (p lt .01)
  • 93 Less Safeguarding (p lt .01)
  • 29 Lower Entropy (p lt . 05)
  • 10 Better on Memory Task (p lt .05)
  • Subjective
  • 64 Less Workload / Effort (p lt .001)
  • 70 More Learnable (p lt .0001)
  • 46 More Confident (p lt .05)

46
Several Thousand Words
47
Experiment Results
48
Ecological Display Experiment Results
  • Mixed-Reality better for
  • Time to complete
  • Number of collisions
  • Subjective workload
  • Entropy
  • Even stronger results for real-world
  • Twice as long
  • Better performance on secondary task

49
Mixed Reality Displays (Pan and Tilt)
50
Control the Information Source, Not the Robot
  • Phlashlight Concept
  • What will UAV see?

51
Semantic Maps and Change Highlighting
  • Video in context
  • Icon-based maps w/ semantic labels
  • That was then, this is now comparison ---
    change highlighting
  • Information decay

52
Information in Context
53
Support Timely Shifts
  • Prompt prospective memory
  • Shift in a timely way
  • Give time to prepare

Situation Awareness
54
Supporting Task Switching Etc.
  • History trails. Knowing recent past helps
  • Tail on a map-based interface
  • Virtual descent into video-based interface
  • Change highlighting/morphing
  • Plans Knowing intention helps
  • Planned path on map-based interface
  • Predicted trajectory on video-based interface
  • Quickened displays
  • Task relationships Knowing relationship between
    two tasks helps
  • Relative spatial location on map-based interface
  • Picture-in-picture on video-based interface
  • Progress bar of task X on task Ys display

55
Improve Perception and Scene Interpretation
(Olsen)
  • Use interaction and machine learning to make this
    robust

56
Future Concept (Proposed)
  • Safe/Unsafe occupancy grids
  • Evolutionary image classifier
  • Evolutionary integration of vision and lasers
  • Particle-based inverse perspective transform
  • Path planning
  • Uncertainty-based triggers for retraining
  • Learning interface mappings from implicit user
    cues

57
Conclusions
  • We can evaluate team feasibility
  • We can predict team performance
  • We need to understand task switching better
  • We need to support realistic task switching
  • Via interfaces
  • Via autonomy

58
Near-Term Future Work
  • Complete validation of task switching experiment
    paradigm
  • Compare new and improved interfaces against
    baseline
  • Compare effects of type and size of interface
  • Answer the questions for the special case
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