Control and Representation Vijay Kumar University of Pennsylvania

A M M W O R K S H O P

John HollerbachOussama KhatibVijay KumarAl RizziDaniela Rus

Control and Representati

on

Vijay KumarUniversity of Pennsylvania

NSF/NASA AMM WorkshopMarch 10-11, 2005

Houston.

NSF/NASA AMM Workshop

Outline

State-of-art Historical perspective (nostalgic memories)

Accomplishments in robot control Summary of last 21 years (WTEC study) Recent, specific contributions (somewhat biased)

Challenges Panelists

Discussion What are the intellectual problem areas we

should address? Infrastructure? Can we can rally around these?


Historical Perspective

40+ years of industrial robotics

>20 years of robotics as an academic discipline

~13 years of mobile manipulation 40 years of industrial robotics

General Motors

1961 Unimate

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Rus Sarcos ARC Hollerbach

Mobility &Manipulation


The Real Agenda for AMM

Mobility Unstructured environments

Manipulation Physical interaction with the

environment Closely coupled

perception/action Not physically grounded Dynamics is important

Autonomy Teleoperation (and therefore

haptics) Supervised Autonomy Autonomy

HapticsJohn Hollerbach

HumanoidsOussama Khatib

Perception/ActionAl Rizzi

Distributed/ModularDaniela Rus


Robotics in the news this week

WSJ, 3/7

“…teleoperation with time delays is a vexing problem in robotics…”

“…because of the lag, it’s inevitable that the human operator will make tiny errors - errors that will in turn cascade into much bigger ones…”


Literature

Domain ~8-10% manipulation ~3-4% grasping ~30-35% mobility

Remaining are on medical, manufacturing, industrial, sensor or “methodology” 500

550

600

650

700

750

800

850

900

1998 2000 2002 2004

No. papers

0

5

10

15

20

25

30

35

40

45

1998 2000 2002 2004

Percentage

Disclaimer: This is not a scientific study!Conferences surveyed: ICRA 1984-86, 1998-2004

Control/representation Model based (~15%) Data driven approaches

(~5%)Counted papers relevant to manipulation and mobility


Literature (Compared to 1984)

Domain ~10% manipulation ~4% grasping ~35% mobility

Remaining are on medical, manufacturing, industrial, sensor or “methodology”

500

550

600

650

700

750

800

850

900

1998 2000 2002 2004

No. papers

Disclaimer: This is not a scientific study!Conferences surveyed: ICRA 1984-86, 1998-2004

Control/representation Model based (~15%) Data driven approaches (~5%)

Counted papers relevant to manipulation and mobility

(40%)

(4%)

(40%)

(3 %)

Total number of papers = 74

~9880 ICRA papers to

date


Major Advances Academic/Government Labs

Inverse dynamics: application of feedback linearization to serial robots, now routinely used in industrial manipulators (e.g., ABB)

Time optimal control: along a path subject to dynamics, velocity and acceleration constraints, also used in industrial manipulators

Adaptive robot control: model based adaptive control with global stability guarantee

Nonholonomic control: control using time varying feedback or cyclic input, application of differential flat system theory, mostly applied to mobile robots and under-actuated robots.

[Wen and Maciejewski, 04]

!!!

!!!

!?

!!!

Disclaimer: Not a survey of accomplishments/needs for AMM


Major Advances (Cont.)

Flexible joint robot modeling and control: Application of feedback linearization to flexible joint robots, applied to some industrial arms.

Teleoperation: wave variable based control for delay robustness. Guarantee stability, but user would feel delayed response.

Order N simulation: Application of order N computation to forward and inverse dynamics. Essential for large number degrees of freedom, e.g., robot with flexible link, micro-robots.

Hybrid force/position, impedance control: Simultaneous regulation of motion and force, applied to machining, assembly, haptic feedback, multi-finger control

?!

!

!!!

!!!


AMM Survey (?)

Mechanics

Design

Control

Manipulation

Multiarm

Planning

ICRA 2000: Grasping and Manipulation Review[Bicchi and Kumar, 2000]

0

2

4

6

8

10

12

14

16

18

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Number of Papers

Saturation of the

area? All problems solvedNot interesting Not relevant


Two other possibilities

Problems are too hard

Or

Nobody is interested in funding this

work!


Significant Accomplishments: Industry

Fanuc

20% market share

1800 employees (1300 in research labs, 10 Ph.Ds)

10,000 robots

Technology provides the competitive edge Before

servo motors/amplifiers Now

collision detection, compliance control, payload inertia/weight identification, force/vision sensing/integration robots assemble/test robots beyond human performance

And mobile manipulation!

Technology transfer does happen!Remember

those ~9880 ICRA

papers?


Results we can build on…(a parochial view)

Modeling/controlling humanoids

Dynamic manipulation and locomotion

Cooperative mobile manipulation

Distributed locomotion (and manipulation) systems

Haptics and teleoperation


Humanoid dynamics and control

Biomechanics for robotics Realistic models Minimum principles leading to

realistic motions

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[Khatib]

Integration (composition) Integrated control of reach

and posture Task space versus posture

space


Humanoid dynamics and control

Whole-body multi-contact control Multiple frictional contacts Models

PostureLegsLocomotion

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[Khatib]


Locomotion and Dexterous Manipulation

Dynamic manipulation and locomotion Intermittent interaction Passive dynamics Reactive control

[Rizzi]

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Significant Accomplishments: Academia

Multiple Mobile Manipulators Multiple frictional contacts Maintaining closure

[Khatib]



[Kumar]

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[Rus]




M3 Modular Mobile Manipulation

Self-organizing, self-assembling, self-

repair Adapt structure Multiple Functionalities Can do work

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[Rus]


Teleoperation and Haptics

High-DOF telemanipulators

Locomotion Interfaces

[Hollerbach]


And yet significant challenges remain!

No successful field deployment of mobile

manipulators Example: Robotic servicing of Hubble

(NAS Committee: Brooks, Rock, Kumar)

ETS-VII (JAXA/NASA) Model-based tele-manipulation Visual servoing for acquisition of non cooperative

targets

No robot (product) capable of physical

interactions in unstructured environment Example: Assistive Robotics


Assistive Robotics

Impact > 5 million wheelchair users* in the U.S. > 730,000 strokes/year (2/3 disabled five

years after stroke), > $50B/year > 10,000 SCI/year (most < 20 yrs old)

Realistic Human-in-the-loop No competing technology

Many other overarching challenges

*Inter Agency Working Group on Assistive Technology Mobility Devices


Current technology

Artificial limbs: peg legs, hook hand Crutches, canes, walkers Wheelchairs Environmental control systems Remote control Many, many customized products

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Significant Challenges, Problems

1. New hardware, systems

2. Modeling/control

3. Composition, synthesis

4. Model-based versus data-based


pHRI: Safety and Performance

>20 cm compliant covering

Challenge: 10x reduction in effective inertia

[Khatib]


Haptic Interfaces and Mobility

Energetic/force interactions between robots and humans Control simulations or real devices Personal assist or amplification devices Rehabilitation or exercise robots

Need haptic interfaces that allow manipulation while walking Psychological argument for VR Need to control robots that can

reach/grasp/manipulate/lean/kick/push

[Hollerbach]


Portable Haptic Interfaces

Body-worn systems Powered exoskeleton Ground-based system

with locomotion interface


Representation and Control

Physics of environmental interaction Distributed interaction

Whole arm/leg/body Task representation for non-rigid

interaction and manipulation Control and task allocation of multi-

function appendages (feet, legs, hands, arms, etc.)

Composition of closed-loop (perception/action) behaviors

[Rizzi]


Composition of Behaviors: Example

Four behaviors (closed-loop controllers) Pre-shape (open/close) Grasp/release Reach/retract Go to (move)


Composition

Pre-shape (close) > Retract


Composition

Retract > Move


Composition

Move || Pre-shape (open)


Composition

Pre-shape (open) > Grasp


Composition

Grasp > Retract || Move


Composition

Move


Composition

Move > Reach > Release


Composition


Distributed Approaches and Modularity

Distributed Control Heterogeneous systems with active modules,

passive modules, and tools for mobile manipulation

Mobile sub-assemblies and hierarchical control

Thanks to Hod Lipson


Future Concept for Modular Robotsin Mobile Manipulation

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Concept: self-assembly withactive grippers and rodsConcept: mobile sub-assembliesnote: mobile manipulation with dynamic kinematic topology forc-space

Concept: self-inspection andself-repair with tools


Distributed Approaches and Modularity Challenges

Control for systems with dynamic kinematic topology Under-constraint systems with continuum of solutions Control for systems with changing c-space Geometrically-driven posture control Control for keeping balance and structural integrity Optimal morphologies for tasks

Uncertainty and Error in Modular Systems Cooperative approach to error recovery in module and

structure alignment, connections, assembly, and repair Dynamical models with uncertainty


Model-based vs. Data Driven

Control/representation Model based (~15%) Data driven approaches

(~5%)

500

550

600

650

700

750

800

850

900

1998 2000 2002 2004

No. papers

Dynamic models are getting more complicated and increasingly sensitive to parameters (uncertainty)

Emphasize completely data-driven approaches


Discussion

Are there a set of basic research questions that We can rally around? Are unique to autonomous mobile manipulation? Are critical? High-impact?

If so, can we create a new research program? How do we sell it? How do we take this to the next step?

Balance basic research high-caliber applied research

How do we make robotics a “big science”?


Intellectual Basis for New Programin Autonomous Mobile Manipulation

Closed-loop behaviors Perception-action loops Vision-based control

Composition of behaviors Sequential Parallel, hierarchical

Task description language Formal semantics

Uncertainty Understanding and characterizing uncertainty Data-driven approaches

Teleoperation and haptics Integration mobility with manipulation

Can it be aTether-esqueprogram?

Control and Representation Vijay Kumar University of Pennsylvania

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