1

Jun UedaAssistant Professor, G.W.W. School of Mechanical Engineering

Adjunct Faculty, School of Applied Physiology

Georgia Institute of Technology

Modularity and Variability in Biologically inspired Actuation

Research topics[1] Biological (real) muscle research

� Exoskeleton for muscle diagnosis

� Human stiffness measurement

[2] Artificial muscle research� “Muscle-like” modular actuators

� Design of compliant mechanisms

2

[3] How can we merge

these two areas?

Generation of

natural movements

Understanding of

muscle coordination

Human sensorimotor

enhancement

Using Operator Arm Stiffness for ImprovedPerformance of Haptic Human-Robot Interfaces

� Problem: When instability is encountered, a human

operator often attempts to control the oscillation by

stiffening their arm, leading to a stiffer system with more

instability.

� Idea: Classify arm stiffness from EMG signals and choose

appropriate impedance parameters of a robot

3

Wearable EMG device

Problem: Instability of haptic interfaces� Power-assisting, or impedance controlled, lift device

� Increase of human stiffness� Co-contraction: Null space of muscle forces � joint-

toques (no change in end-point force)

4

Co-contraction of antagonistic muscle pairs

� Antagonistic muscles contract together � Higher stiffness

� Can calculate angle between moment arms

� Angles closer to 180°more antagonistic

� Calculated using exiting musculoskeletal model

Wrist (Flexor Carpi Ulnaris, Extensor Carpi Ulnaris) �127 degElbow (Biceps Brachii, Triceps Brachii) �163 deg

EMG measurement and stiffness classification

6

Measure agonist-antagonist muscle pairsWrist (Flexor Carpi Ulnaris, Extensor Carpi Ulnaris)Elbow (Biceps Brachii, Triceps Brachii)

0 5 10 15 20 25 30

EMG (BB)

EMG (TB)

EMG (ECU)

EMG (FCU)

Nom Angle

Nom Force

% of Variance of Stiffness

Pre

dict

or V

aria

ble

Predictor Variable Effect on Stiffness

2

7

Rigid Surface Contact Comparison

Stiffness compensation onStiffness compensation off

298 300 302 304 306 308

0.3

0.35

0.4

0.45

0.5

0.55

0.6

Time (s)

Pos

ition

(ra

d)

Rigid Surface Contact with Stiffness Compensation

Highlight indicates compensation for high arm stiffness

262 264 266 268 270 272 274 276 2780.3

0.35

0.4

0.45

0.5

0.55

0.6

Time (s)

Pos

ition

(ra

d)

Rigid Surface Contact without Stiffness Compensation

8

� Healthy participants (n=20)

� Average time of a pick-and-place

task was reduced by 25%

� High variability between subjects (and trials)

� Histogram of all measured stiffness levels shows a Poisson-type distribution

1 m

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Tim

e to

Bes

t Pla

cem

ent (

s)

(Nor

mal

ized

by

Dis

tanc

e to

Tar

get)

Controller State

Time (Normalized)

0

2

4

6

8

10

12

Dis

tanc

e of

Bes

t Pla

cem

ent f

rom

Tar

get

(cm

)

Controller State

Distance

Off

On

Performance test: Pick & Place Task

9

0 2 4 6 8 100

10

20

30

40

50

60

70

80

Stiffness (kN/rad)

Fre

quen

cy

Stiffness Level Histrogram

Im Is

VsVm

ZsZm

Um UsZenv

Zop

Vop

Operator Robot

Work

Operatorimpedance

Se

nso

r D

ata

Probabilistic estimation of muscle co-contraction: in-progress

10

Probabilistic prediction of

muscle co-contraction

S1 SnS2

1β

ImpedanceGain scheduler

2

0 argmin∑

=

j j

j

PCSA

f

ff

⋅<≤⋅=

jj PCSAkf0

fAτ

)()(0 tβAAIff +−+=

subject to

Hypothesis: (parameter of muscle coactivation) is a random variable & whoseprobability density function (PDF) can be predicted by means of Markov models.

)(tβ

11

Robot-assisted diagnosis of neurological movement disorders

� Exoskeleton robot can induce a wider variety of muscle activities than performing conventional tasks.

� Need to understand human-robot physical interaction at the level of individual muscles.

Exoskeleton has more “hands.”Control is more accurate.

12

Joint torques (e.g., by motion capture) Muscle forces

Optimality principles in redundant muscle coordination

1

( ) min

. .0

rn

j

j j

i

fu

PCSA

s tf k PCSAj

τ=

= →

= ⋅ < < ⋅

∑f

A f

Hypothesis : Brain coordinates redundant muscles to minimize a cost function

R. D. Crowninshield et al, J. Biomechanics, 1981.

1f3f

2f

1τ

2τ4f

5f

6f

Crowninshield’s law

fAτ ⋅=

=

2

1

ττ

：Muscle moment arm

：Muscle force

Af

SIMM, Musculographics inc. AnyBody , AnyBody, Tech. Ueda et al., 2007

3

Robot-assisted muscle isolation� Neuromuscular function test, therapeutic training, power-

assisting, muscle fitness training, …

� Can be boiled down to a single question:

13

How can we determine an adequate exercise that induces a desired change in a target muscle force?

13

Target muscle anddesired forces

Robot torque ??

Biceps = 15 [N] (x 1.5)

Biceps = 0.5 [N] (x 0.5)

or

orBiceps = 1.2 [N] (x 1.2)

min→

∑

j

r

j

i

PCSA

f

Tip-force ??

�Inverse solution of muscle force prediction based o n the optimality principle

Constraints for the cost function ?14

Theorem ：Desired muscle forces can be realized if the exoskeleton applies

Individual muscle force control - Complete Solution -

(3) Inactive muscle forces are still zero

(1) Muscle forces are positive

(2) Desired muscle forces are completely realized

Feasibility conditions ：

Target muscles are perfectly controlled

Minimize changes of non-target muscles

Jun Ueda, Ming Ding, Vijaya Krishnamoorthy, Minoru Shinohara, Tsukasa Ogasawara, "Individual Muscle Control using an Exoskeleton Robot for Muscle Function Testing,” IEEE Transactions on Neural and Rehabilitation Systems Engineering, 2010.

15

Force-matching motor tasks

Desired tip-force

Measured tip-force

Muscle control experiments

16

-0.6

-0.4

-0.2

0

0.2

0.4

BRA BRD

ECU

-0.6

-0.4

-0.2

0 0

BRA BRD ECU

Desired Muscle force

Modified Muscle force (EMG)

B:

BRA x 0.5BRD x 0.5ECU x 1.0A:

BRA x 1.0BRD x 1.0ECU x 1.3 C:

BRA x 0.5BRD x 0.5ECU x 1.3

Ratio of change

-0.4

-0.2

0.2

0.4

0 0

BRA BRD

ECU

Assist

Resist

0

(1) Elbow joint 90 deg(2) 2kg iron weight(3) 6 healthy subjects(4) Surface EMG measurement

Pneumatically-Powered Robotic Exoskeleton to Exercise Specific Lower Extremity Muscle Groups in Humans

• Exercise Anti-Gravity Muscles (Legs and Lower back) to shorten rehabilitation time when returning to a higher gravity environment

• Minimize Bone Losses since some loss may be permanent

Desired force profiles in atarget muscle

Research topics[1] Biological (real) muscle research

� Exoskeleton for muscle diagnosis

� Human stiffness measurement

[2] Artificial muscle research� “Muscle-like” modular actuators

� Design of compliant mechanisms

18

[3] How can we merge

these two areas?

Generation of

natural movements

Understanding of

muscle coordination

Human sensorimotor

enhancement

4

“Muscle” Actuator Materials and Biological Muscles

Stress

PZT SMA

Reliability,Stability

Efficiency Speed

Strain

PolypyrroleConductingPolymer

ElectrostrictivePolymer

(Elastomer)

0.1%, ~ 10ms

[2] Marieb, E. Human Anatomy and Physiology Benjamin Cummings, 2001

Compliant TissueCellular Structure (non-uniform)Quantized (on-off) controlVariability (noise) 19

“Nested Rhombus” Exponential Strain Amplification

PZT stack actuator

0.1%

1.6%

23.9%

1.6%

0.1% x 15 x 15=22.5%!

Our goal: 20% (Amplification gain)^Layerby “Power-law”

21% effective strain, 1.7N, 15g

20Ueda, Secord, Asada, IEEE/ASME Transactions on Mechatronics, Vol. 15, No. 5, pp. 770-782, 2010.

MRI-Compatible Self-sensing Piezoelectric Tweezers

Possible tele-surgery device• Nonmagnetic (MRI compatible)• 1N, >30Hz • Self-sensing of force/displacement

21

Self-sensing Piezo Tweezers

PZT

Differential amp Charge ampTrue valueSensor output

Actuator

Sensor

Yuichi Kurita, Fuyuki Sugihara, Jun Ueda, Tsukasa Ogasawara, "Piezoelectric Tweezers with Force- and Displacement-Sensing Capability for MRI, " IEEE/ASME Transactions on Mechatronics, in Press,

Amplifiedtip-force

Original tip-force

22

Bilateralcommunication

Nonmagnetic sensing system (Georgia Tech)

MRI compatible haptic device

MRI compatible fluid actuator (Vanderbilt)

23

3-spring static lumped parameter model

aloadk

Jk

pztf

pztk

1x∆BOk

BIkPZTk

loadk

PZT stackactuator

pztf

pztf

1x∆

1f1f

24

pztf

1f

pztx∆

1x∆ 2-port network model

∆∆

−−

=

−−

1132

21

1 / x

x

XXX

XX

f

f pztpzt

5

Nesting of two-port network models

25

� Outermost (n – k) layers dominate stiffness � Nesting Theorem

1I 2I

2V1V

1I 2I

2V1V

2I

2V

2I

2V

Load

Load

Unit 1

Unit 2

Unit n

PZT

2nd

laye

r am

plifi

catio

n m

echa

nism

1stla

yer

mec

han

ism

Act

uato

r ar

ray

with

nun

its

Schulz and Ueda, Invited Session- Bio-inspired Systems, Paper WeAT5.4, 11:00-11:20

Hill-type Muscle Model

Muscle-like compliance (Hill model??)

PZTf

PZTk

PZT stack actuator

Amplification mechanism

Lumped Model for Piezoelectric Cellular Actuators

26

Human Skeletal Muscle possesses:

� Quantization due to finite enervation rates

� Resonant modes due to flexibility and mass of muscle tissue.

However, despite these effects, humans are able to produce smooth motion with simple on-off commands to discrete groups of muscle fibers.

The biological mechanisms by which the switching times are chosen are not well known.

Physiologically-inspired impulse excitation method for fast and compliant actuators

Quantization in actuation (# of motor units) butHigh precision in time (very fast contraction time)

27

Vibration suppression by redundant ON-OFF actuation

0 5 10 15-1

0

1

2

3

4

Time [s]

Dis

plac

emen

t [m

m]

(1) Serially-connected PZT actuators (redundant discrete actuation)

(2) Fast response of PZT (high resolution in time)� Linear actuation (amplifiers) may not be necessary

Motivation

28ON-OFF

ON-OFF Power Switching Network

Step Minimum switching method (proposed)

Open loop-control (point-to-point movement)

Joshua Schultz and Jun Ueda, "Experimental Verification of Discrete Switching Vibration Suppression," IEEE/ASME Transactions on Mechatronics, in Press.

37% energy efficient

30

Biologically-inspired Robotic Vision

θ

tQua

ntiz

edC

omm

ands

“Saccadic” camera movement for quick panoramic composite photo

Object tracking by smooth-pursuit

Flexible structure (actuator)Quantized control

High-speed switching

6

31

Closed-loop control (smooth-pursuit)

0 0.2 0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Time [sec]

Pos

ition

[m] Reference

PWM QuantizationIntersample quantized

0 0.2 0.4 0.6 0.8 1 1.2

Qua

ntiz

atio

n le

vel

Time [sec]

0

2

4

-2

-4

-6

-8

OFF

ON

Intersample quantized

Joshua Schultz and Jun Ueda, the 2010 Dynamic Systems and Control Conference (DSCC'10), Boston, MA, 2010.

0

3

1

2

T 2T

Nominal input (discrete-time control theory)PWM QuantizationIntersample Discretized

(1)

(2)

(3)

OFF

ON

Solving optimization problem

−−

1

1

3&

1

2

3&

1

2

3&

1

2

1&

L

L1 L2 Lk-1 Lk

L1 L2 L3 L4

−−

1

1

F1&

000

122

8&6&1&

000

211

01&E&1&

0

5

1&

L1 L2 L3 L4

L1 L2 Lk-1 Lk

Connection Fingerprint (programmable!)

(a)

(b)

“Fingerprint” method for modeling and characterizing complex actuator array topologies

David MacNair and Jun Ueda, "A Fingerprint Method for Variability and Robustness Analysis of Stochastically Controlled Cellular Actuator Arrays," The International Journal of Robotics Research, Volume 30, Issue 5, pp. 536 - 555, April 2011. 32

2 Cells : 2 Arrays

Automatic generation of actuator topologies using the fingerprint method

plot_fingerPrintGrid(fingerPrintBuild(# of cells)) 33

3 Cells : 4 Arrays

34

5 Cells : 23 Arrays

35

7 Cells : 199 Arrays

36

7

Non-uniform array response: twitch to tetanus

37

0

0.1

0.2

0.3

0.4

0.5

For

ce (

N)

0 5Time (s)

Cell 2, 3, 4Cell 8

Cell 10, 11

Cell 1, 6 Cell 5

Cell 7

Cell 9, 14Cell 12, 13

(a) single cell activations

0

1

For

ce (

N)

Time (s)

Cell #2 #2

#10#2

#10

#8

#11#3

(b) Multiple cell activations

11

2

3

4

5

6

7

8

9

10

11

12

13

14

Research topics[1] Biological (real) muscle research

� Exoskeleton for muscle diagnosis

� Human stiffness measurement

[2] Artificial muscle research� “Muscle-like” modular actuators

� Design of compliant mechanisms

38

[3] How can we merge

these two areas?

Generation of

natural movements

Understanding of

muscle coordination

Human sensorimotor

enhancement

39

ON-OFF ControlledSMA cellular actuators

Generation of “biological” movements

NSF CPS #0932208PI: Ueda, GT-ME

Musculoskeletal model

�����

Quantizer

ON-OFF switchingnetwork

Controller ����

)(tα )(tαTrajectory generator

Embedded sensor information

End-pointposition

Analog motor command

Quantized motor command

OFF

ONModulated impulse commands

Flexible muscle model

α

Physiologically-inspired quantized control

Henneman’s size principle?

Optimization criterion

min)()( →−∫+Rt

t d

f

f

dttt rr

• Non-uniform motor units• Achieving a fine resolution for a small

motor command and a more coarse resolution for a larger command

�

� �

�

(a) Uniform quantizer (b) Logarithmic (floating-point) quantizer

� �Neuronal variability?

41

Neuronal variability and Signal-dependent noise

Uno, Y., Kawato, M. & Suzuki, R. Biol. Cybern. 61, 89–101 (1989).Observation: human-arm trajectories, 4 trials

Signal-dependent noise (Harris and Wolpert, 1998): Standard deviation of force variability is proportional to mean motor command

Harris, and Wolpert, Signal-dependent noise determines motor planning, Nature, 1998

0 0.5 10

0.5

1

Motor command

Act

ivat

ion

leve

l

Criticisms:(1) Standard deviation of neurons’ firing rate is proportional to the mean rate to the power of 0.48.(2) What is the source of signal-dependent Gaussian noise?

Signal-dependent noise or quantization error ?

� Do we need a source of Gaussian noise in robots to mimic the

neuromotor variability?

� Henneman’s size principle �Floating-point quantization

� Our result: if an actuator array is floating-point quantized, its variability statistical is statistically equivalent to that of

proportional “signal-dependent” noise.

42

1 2 4 8

Activation level

Activation level

1 1 1 1 2 2 4 41 1 1 1 2 2 4 4

(a) floating-point segmentation with 1-bit mantissa (= binary segmentation)

(b) floating-point segmentation with 3-bit mantissa

8

Proof

43

Qx 'x

- +

FLν

compressorexpande

r

- +ν

Uniform quantizery 'y

FLQ Floating-point quantizer

Signal-dependent noise(Gaussian proportional to mean command)

Floating-point quantization

Equivalent if

0 0.5 10

0.5

1

Motor command

Act

iva

tion

leve

l

0 0.5 10

0.5

1

Motor command

Act

ivat

ion

leve

l

B. Widrow, Quantization noise, Cambridge University Press, 2008

44

0 0.2-0.2

0.5

0.4

0.3

Generation of biological movements: in-progress

Cellular actuator structure � Quantized noise in actuation

x [m]Uno, Y., Kawato, M. & Suzuki, R. Biol. Cybern. 61, 89–101 (1989).Observation: human-arm trajectories, 4 trials

min)()( →−∫+Rt

t d

f

f

dttt rr

Muscle-level comparison

45

Pec MajorBrachialisLatTriceps BrevBicepsTriceps Long

0 0.1 0.2 0.3 0.4 0.5 0.60

0.1

0.2

0.3

0.4

0.5

0.6

Time [s]0 0.1 0.2 0.3 0.4 0.5 0.6

Time [s]

0

0.1

0.2

0.3

0.4

0.5

0.6

Nor

mal

ized

mus

cle

activ

atio

n le

vel

0 0.1 0.2 0.3 0.4 0.5 0.6Time [s]

0

0.1

0.2

0.3

0.4

0.5

0.6

Harris’s proportional SDN Floating-point quantization Uniform quantization

� Proportional SDN, floating-point quantization, uniform quantization

-0.2 -0.1 0 0.1 0.2 0.30.2

0.25

0.3

0.35

0.4

0.45

0.5

X[m]

Y[m

] Proportional SDNFloating point quantized

Uniformly quantized

T2

T6

Sensorimotor Enhancer� Tactile receptors: nonlinear threshold systems � Stochastic resonance (application of subsensory

white noise) improves the sense of touch� Improve motor functions

46

Dr. Shinohara, Applied Physiology

Kurita, Shinohara, Ueda, ICRA 2011

Noise-enhanced human sensorimotor function

Khaodhiar, et al., 1991 H. R. Dinse et al., 2005

Collins, IEEE EMBC Mag. 2003

Challenges• Dexterity and force control of

fingers• Wearable (compact & palmar

side should be free)• Optimal design• Orthotic devices for persons with

peripheral neuropathy• Surgery training, Manual

assembly , texture design, etc.48

Experimental results

� Sensory test (Texture discrimination )

� Motor test (grasping test)

Higher sensitivity

Less effort(more efficient)

9

Transmissibility characteristics of a fingertip

49

Gain

Phase

• Vibration is attenuated by viscoelasticity of skin and subcutaneous tissue.

• Measured by Laser Doppler Vibrometry.

Challenges and opportunities

� Artificial systems and biological systems

� Quantized and compliant structure

� Modulated impulse commands

� Source of variability (noise)

�Wearable robotics

� Understanding of human-robot physical interaction at the level of muscles

� Dynamic and probabilistic modeling

� Sensorimotor systems and dexterity

� Evaluation of “human-like” movements?50

Acknowledgement� National Science Foundation (CPS-0932208, CNS-1059362, IIS-1142438)

� NSF Center for Compact and Efficient Fluid Power

� General Motors

� Korea Institute for Advancement of Technology

� Japan Science and Technology Agency

� RIM@GT, GT/Emory Health Systems Institute

51Thank you !!51

Dr. Yuich KuritaJoshua Schultz, MEDavid MacNair, RoboticsBilly Gallagher, RoboticsGreg Henderson, ME Melih Turkseven, ME Timothy McPherson, MEEllenor Brown, AP