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Human-Robot Teams Chris Human-Robot Teams Chris

Human-Robot Teams Chris - PowerPoint Presentation

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Uploaded On 2018-03-06

Human-Robot Teams Chris - PPT Presentation

Atkeson CMU With input from Florian Jentsch UCF and Jean Oh CMU Robotics Collaborative Technology Alliance RCTA and Katia Sycara CMU DARPA Robotics Challenge DARPA Robotics Challenge Lessons Learned ID: 640805

human robot robots teams robot human teams robots attention autonomy predictable natural control simple behavior interaction errors models command humans 2020s systems

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Slide1

Human-Robot Teams

Chris

Atkeson

, CMU

With input from Florian

Jentsch

, UCF

and

Jean Oh, CMU

(Robotics Collaborative Technology Alliance (RCTA), and Katia

Sycara

, CMU. Slide2

DARPA Robotics ChallengeSlide3

DARPA Robotics Challenge: Lessons Learned

www.cs.cmu.edu/~cga/drc

Operator errors dominated failure causes

Software must detect and handle operator errors.

Safety software is a major source of fatal bugs. Example: fall false alarm causes robot to fall down “safely” when robot could have not fallen at all.

Operators want control at all levels

“Nudging” at various levels and in various coordinate systems useful.

Operators not particularly interested in autonomy.

More important to design robot to be easy to drive than to design for autonomy or autonomous performance.

Need to protect robot from operator.

Design for operation with multiple subsystems not working.

Autonomy Valley: It gets worse

before it gets better.Slide4

“Go to traffic barrel

b

ehind building”

Toward Mobile Robots Reasoning Like

Humans,

Oh, et al, AAAI 2015Slide5

Human Robot Team Interaction (HRTI):

Now and 2020s

Humans with tools

Carefully engineered, brittle, erratic

Gets stuck, repeats errors, crashes or fails catastrophically

Robotic interface

Robotic (inscrutable) reasoning

Literal command following

Isolated mission and individual learning: wheel reinvented every day

Object recognition

Vision, speech

Information flood, feedback and mind-sharing are distractions

Individual sensor-based perception, crude sensor-fusion

Individual attention, plans, uncoordinated thinkingHRI on lab prototypes, non-working systems

Human-”animal” teams

General, robust, predictable behavior

Metacognition: is something about to or going wrong?

Human-like interface, natural language

Human-like reasoning, transparency

Objective/intent achievement

Training/mission loop: Life-long shared team learning

Affordance recognition

+

audition, whole body tactile, smell

Minimal task-relevant feedback highlighting unexpected events

Team perception: multi-modal, multi-observer sensor-fusion

Group attention, planning, mind

HRI on functional state of the art systems, universally applied.Slide6

Human-Robot Teams:

Research Issues for the 2020s

Flexible task allocation

Adaptive autonomy

Adjustable autonomy

Mixed Initiative

Mutual trust

Mutual monitoring and supporting behaviors

Mutual predictability and intent inferencing

Establishment and maintenance of common ground

Ability to redirect and co-adapt

Human-robot

t

eamwork metricsSlide7

HULC

XOS3Slide8

Physical Human-Robot Interaction (PHRI

)

Wearable robots/exoskeletons

Now and 2020s

Assist/Carry load

Failure of XOS (power)

Failure of HULC (tiring)

Small number of examples of successful assistance of typical subjects

Small

# of actuated DOF

Human actuates most

DOF (with approx

. 700 skeletal muscles

)

Get out of the

way control/neutral gear to provide freedom of movement

Minimize

limb encumbrance

Predictable, not smart

What is load? Armor, Supplies, (Active) sensors, Additional (somewhat autonomous?) arms and hands, Additional legs, Weapons

Blurring of distinction between on-body robots and disconnected robots and vehicles.

Ironman suit comes after simple suit, if at all.Slide9

Modeling individuals and teams

Every soldier has a phone/part-of-uniform/assistive-device/wearable-robot from basic training onwards.

Capture all experiences (Lifelog, …)

Capture all training (Classroom 2000, …)

Physical and skill models: What does the user know? What can the user do? Current state (fatigue, conditioning, ...)

Cognitive models: Know? Do? Current state (distraction, attention, …)

Motivational

models (energy/fatigue, …)

Can we more effectively coordinate teams if we have teammate models?

Can we transfer knowledge across teammates? Across teams? Across time? Across situations? Between humans? Between robots? Between humans and robots?

Can we advise and or coach individuals and teams? Personal trainer? Team trainer?Slide10

Smart/Natural/Trustworthy or Simple/Predictable/Symbiotic

Physical Human-Robot Interaction:

There is no existing model for how a “natural” exoskeleton should work. The field is dominated by the dream of an “invisible” suit that gives us super powers but otherwise does not restrict us in any way (Ironman). We can’t build that suit with existing technology.

An alternative design goal is a limited exoskeleton that provides predictable behavior on the short time scale, and adapts to the user over longer time scales (“symbiotic”, such as an intelligent/adaptive bicycle, skateboard, windsurfer, …). The human operator learns to provide rich behavior on top of the underlying simple and predictable system.

This debate applies to informational interaction: natural interface vs. symbiotic. Maybe a simple game-like interface is better in some cases?

This debate applies to how smart a robot should be. Maybe stupid is better

in some cases?

This debate applies to how trustworthy a robot should be. Maybe a robot is trustworthy because its behavior is simple and predictable, not because it autonomously does a complex job well

.

Animal model: should goal be doglike behavior?

Should natural, smart, and trustworthy be goals for the 2020s, or are symbiotic, simple, and predictable more realistic goals for the 2020s?Slide11

One size does not fit all

What are the tasks?

What are the environmental characteristics?

What robot abilities are needed?

What human abilities (physical, cognitive, and attentional) are needed?Slide12

Research Questions for

Mulit

-Robot HRI

How can people control multiple robot teams of increasing size?

What is the density of robots a human(s) can control?

What kinds of command are possible for a particular density?

Key Constraint: Human attention is limited; attention is the budget

How many “things” can a human manage?

R

obots

Tasks

Other people

Sources of informationSlide13

Many forms of interaction are needed

Robots

must be individually controllable

must function autonomously for long periods

must be

commandable

as cooperating teams

must adapt to absence of human attention

must incorporate humans in autonomous plans

Key Idea: Look at HRI from viewpoint of complexity of operator’s cognitive complexity of command

This framework allows systematic study of human control of multi-robot systemsSlide14

As size grows, complexity of command domina

tes

O(1)

O(m)

O(>m)

Cognitive limit

# of RobotsSlide15

O(n)

Fan-out models

Independent Robots

Individual automation

Scheduling attention

Organizing multiple

operators

O(1)

swarms or centralized control

Autonomously

Coordinating

Expressing goal

Recognizing satisfaction

O(>n)

Playbook

Machinetta

Assisted

Coordination

Initiate

Recognize plans

Modify

HRI for Robot TeamsSlide16
Slide17

“Go to the left of the building.”

Robot receives natural language command from human.

Robot reports status back in natural language.

Semantic perception

Language grounding & PlanningSlide18
Slide19

Ease

Of

Use

None Autonomy Full

Systematic

Errors

Low Frequency

Random Errors

Autonomy Valley