Human Robot Teams: Concepts, Constraints, and Experiments Research Agenda Evaluation Technology

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Human Robot Teams: Concepts, Constraints, and Experiments


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


The Presentation Agenda



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?



A More General Case: Span 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


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?



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



Neglect Tolerance: Neglect 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?



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



Can a human manage team T ? Fan-out and Feasibility

  • Fan-out (homoeneous teams)

  • Feasibility (heterogeneous teams)

  • These are upper bounds



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



Neglect Impact Curves

  • A task is Neglected if attention is elsewhere

  • Neglect impacts task performance: 2ndary tasks



Not Neglect Tolerant Enough



Too Neglect Tolerant

  • Old Glory Insurance



Interface Efficiency Curves

  • Recovery from “zero” point

  • Imprecise switch costs



Efficient Interfaces

  • PDA-based UAV control (versus command line)



Efficient Interfaces

  • Phycon-based UAV control (versus command line)



Finding NT and IT from the curves



Example



Validation of Method: Complexity

  • As complexity goes up, NT goes down and IT goes up

  • Feasibility using NT/IT needs more work



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



Existing Tradeoffs



Types of Autonomy



Using Tradeoffs to Select a Configuration



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



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



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



Predicting Performance continued …



Using Predicted Performance



Accuracy of Predictions in a Three-Robot Team



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



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


Before and After



Getting a Feel for the Experiment



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



Preliminary Results

  • 6 subjects, none naïve

  • 207 correct change detections

  • One-sided T-test, equal variances



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”

  • Probably spatial reasoning and changing perspectives



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


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



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 won’t help enough


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



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


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



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



Real World Results

  • Objective

    • 51% Faster (p < .01)
    • 93% Less Safeguarding (p < .01)
    • 29% Lower Entropy (p < . 05)
    • 10% Better on Memory Task (p < .05)
  • Subjective

    • 64% Less Workload / Effort (p < .001)
    • 70% More Learnable (p < .0001)
    • 46% More Confident (p < .05)


Several Thousand Words



Experiment Results



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


Mixed Reality Displays (Pan and Tilt)



Control the Information Source, Not the Robot

  • Phlashlight Concept

  • What will UAV see?



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



Information in Context



Support Timely Shifts

  • Prompt prospective memory

  • Shift in a timely way

  • Give time to prepare



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

  • 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 Y’s display


Improve Perception and Scene Interpretation (Olsen)

  • Use interaction and machine learning to make this robust



Future Concept (Proposed)

  • Safe/Unsafe occupancy grids

  • Path planning

  • Uncertainty-based triggers for retraining



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


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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