INTERPRETATION OF SPONTANEOUSLYPROVIDED TACTILE INSTRUCTIONS

Fabio Dalla Libera,
JSPS Postdoctoral Fellow atIshiguro Laboratory,
Osaka University, Japan

2013 Feb 22Micheletto laboratory

Outline2Teaching by touching
Tactile instructions as a communicationmeanDifficulties

Proof of concept implementation
ExperimentAnalysis of the data collected

Design policies for building touch interpretation
algorithms

Klinokinesis based control Analysis of natural looking movements
Part 13Teaching by touchingWhy humanoids?
 They can usetools andinfrastructures
already availablefor humans
 They can convey
non-verbal
information: easecommunication
 They can becomea model forunderstanding
humansDifficulties


Developing motions forhumanoids with high number ofDOFs (≥20) is
difficultContinuous
state space

Continuous
action spaceTimecontinuous

連続状態

連続行動

連続時間

• Automatic learning isoften impossible

Highlydimensional
spaces
高次元空間
離散
でもTransfer of knowledge
1.

Task known beforehand:
Programmer’s
knowledge includedin the control
algorithms
Programmer's
knowledge

プログラマの知識

Examples:• Visual servoing• ZMP• CPG connections• Design of the state/action space

Robot’scontrollerTransfer of knowledge
2. Task partially known beforehand:

Programmer'sknowledge

一般ユーザ
User’sTask knowledge

Symbols Database
Modules /motion primitives

Selectionand composition

Examples:• Teaching by watching• Mimesis modelTransfer of knowledge
3. Task unknown beforehand:
The user executes
the motion byhim/herself

Examples:• Motion retargeting

The user makes
the robot executethe motion

Examples:• Motion editor• Kinestetic demonstration(teaching and playback)Commercial motion editors
9Movement definedby keyframes
 The user needs toset the position ofeach joint at eachkeyframe.
 Time consuming andunintuitiveKeyframe based motion description
Motion is a sequence of“important postures”
defined for certain times
 The postures at intermediate
in between are obtained byinterpolation
Keyframe 1

Interpolations

動作表現 = 一連の基本姿勢

Keyframe 2time

Touch as a communication mean
Human - human motion teaching
Human – robot
motion teaching

Tactile protocol
12

Passive movements determined by
the joint compliance
The teacher sees therobot and hasa desired modification+The teacher
Intuitively provides
touch instruction

Lead-throughteaching

Passive movements

Interpretation of theinstruction meaning
and motion modification

Active, arbitrary
movementof the robot’s
motorsKinestetic
demonstration

Interpretation of tactile instructions
13

Context dependence

User dependenceSame touch, different meanings
depending on the robot’s
postureSame touch, different
meanings
depending on the user

Robotstanding

Robotsquatting

Interpretation:possible approaches
1) Fixed protocol あらかじめ決められたプロトコル

Desired motion
modification

教えたい
動作調整

Conversion torequired touch instructions

User’smental effort

決められた命令に変化

Touchinstruction

Intetrpretation according
to the mapping

Motionmodification

接触教示

プロトコルに基づく解釈

動作の調整

ユーザが考えることが必要

2) Human interpretation modeling 人間の学習者のモデル化に基づくプロトコル

Extraction of a modelof human learners’
interpretation

人間のモデル化

Desired motion
modification

Intuitively provided
touch instruction

Interpretation of theinstruction meaning

Motionmodification

教えたい
動作調整

直感的な
接触教示

教示者の意図推定

動作の調整

Motion development
15

Observe the motion

Input

OutputOnline updateof the protocol
Select a keyframe

Touch the robot

Provide correct interpretation

Correct interpretation

Wrong interpretation

Teaching a new instruction
16Learning algorithm
17

Input
Touch instruction + context
Touch sensor values:
A vector of continuous
values in the range [0,1]Learning by Kernel regression:
non-parametric (instance based)-Posture:
local learning
Joint potentiometer values- Orientation:
Accelerometer values

OutputMotion modification

Numberofexamples

Posture change for current
sensory input

Current sensory
input

Kernelfunction

A modification of the posture assumed by the robot at the time it is touchedi-th example
sensory input

Posture change ofi-th example

Kernel function
183

Touch sensor
data

Set of sensors
pressed

12Sensory information
except touch sensors
1Stronger push → bigger effect2

Closer context → stronger
contribution to the output

3

Only a subset of sensors
is pushed → ignoreExperimental setup
19

M3 Neony: body covered with 90 opticaltactile sensors

Subjects:• engineering
students• age 23-25• 3 males, 1female• no knowledge
on TbT

Task:
Development ofalgorithm exercise

Results20



On average
 96 instructions (min 60 – max 157)
 867 touches (min 436 – max 2332)

Corrected responses ratio 6 hours (min 4:20 – max 7:30)

Meanings Taught
Instructions Used

The system correctly
learns how to interpretthe touches and theuser needs to teach lessand less the meaning of
the touches

Touch number

Data analysis
21Study how humans use touch tocommunicate
MappingStudy the properties of the mapping Design policies for interpretation algorithms

MappingMapping linearity
22

Linear (ridge) regression generalizes very poorly Errors often 2 orders of magnitude w.r.t. Kernel
regression
 Regression trees can improve the situation,
exploiting the context


P8>θ8
P2>θ2
P3>θ3

P5>θ5
P9>θ9

O2>ɸ2- Posture(potentiometers)- Orientation(accelerometers)

P1>P1

1

Context that mostly influence themapping:rotation of the joints near the torso

Mapping structure
Torso
Head
Rightarm
Leftarm
Rightleg

Generally mutual information
is high for sensors and motorsof the same limb

For high level behaviors,
ex: push the head = squathigh mutual information
between different parts

MotorsLeftleg

Rightleg

Leftleg

Head
Rightarm
Leftarm

Sensors232
Mapping algorithms• can assume a higher
probability for motors of thesame limb
• must allow inter-limb
associations

Mapping sparsity
24

Very high Gini index
for inputs and outputs

Friedman, Hastie, Tibshirani, Regularization paths for generalized linear models via
coordinate descent, J. of Statistical Software, 33(1), 2010.

Subject 4

0.96

0.90

0.98

0.92

0.82

0.78

0.87

0.70

Prediction error~

chosen by Elastic net regularization

Subject 3~with rows

Subject 2

Given the couples ( , ) for 1≤i ≤epredict the resultfrom~

Mapping

Subject 1

Increasing sparsity enforcement αThe mapping between inputs
and outputs
can be improved enforcing sparsity

3Motion Primitives in Motor responses
25

Motion responses (“edits of a single posture”)

Keyframes (“whole motion”)

Head
Rightarm
Left
arm
Right
leg

Motion primitives“Keyframe-derived” motion primitives
and “motor response-derived” motion
primitives are very similar

Left
leg

Motion primitives

averageprediction error

Left
leg

Rightleg

Head
Right
arm
Left
arm

“We can use “seen keyframes” forimproving the mapping
Previous motor responses
All motor responses
Previous keyframes

dimensions

4

Keyframes selection
26

Mapping
Projection on
span <F1,..,Fk>~

Initialprediction~
chosen by minimizing~

Prediction error

H. Lee, A. Battle, R. Raina, and A. Y. Ng. Efficient sparse coding algorithms. NIPS, 2007.

Increasing sparsity enforcement

Projecting on a subset of
keyframes (instead of thewhole) can further
improve the performance5

User dependency
27

User 1 database

Kernelregression

User 2 database

User 3 database

Kernelregression

Kernelregression

Distance between user pairs

Visualization through MDS

Different levels of abstraction
28

high-level / symbolic(e.g. “touch right knee” =“move right leg forward”)

Imagine to have elastic
joints (passive / pin anddrag model)“Arbitrary” association
between sensors and close
jointsDifferent people tend to associate different“abstraction levels” to touch instructions
6

Active - passive
29

Passive movements=passive

Users distribute over
the whole range from“passive” convention to“active” convention
7

User=activeTbT: A conceptual schema
30

Way of teaching

Humanbrain

Intendedmotion
response

Responses
taughtPhysicalworld(abstraction level)

Perceived
context
Task

Providedtouch

Physicalcontext

Motion(frames)

TbT analysis: Conclusions
31Context and user dependence Motion responses involve with high probability
joints of the touched limb

 Need the possibility of modeling exceptions and inter-limb mappings
 Can assume sparse movements have high probability


Motion responses can be composed by motor
primitives
 With good probability they can be derived fromcombining few frames

Predictability of CPG’s behavior:
Oscillator type
Plenty of oscillators types:•Sinusoidal (Morimoto et al. 06)•Hopf/adaptive Hopf(Righetti et al. 06)•Rayleigh (De Pina Filho et al. 05)•Van der Pol (Veskos et al. 05)•FitzHugh-Nagumo (Collins et al 94)•Hopfield (Mathayomchan et al 02)•Matsuoka (Inada et al 03, Inoue et al 04)•…

異なる初期状態

根本的に異なる行動

Generality of generable movements
CPG network structure
Chain
LegendM

M

M

M

Oscillator

Tree

Star
M

M

M

M

M

M

M

M

M

M

M

Full connections

M

M

M

M

M

Actuator
Connection
Betweenoscillators
Control ofan actuatorby anoscillator

M

Body part• Literature presents
a great variety ofnetwork structures• They can besummarized in 5types• The structure
determines thetypes of motions
that can begenerated
CPG[神経振動子]の相互接続によって出力の一般性が増す

Homologous joints

MMMMM

MMM

System setup
Neuron model:
Hopf oscillator

Collision prevention:
Enclosing convex
Polyhedra
実体より大きい凸多面体で近似する

Configuration:
Star(“clock” oscillator)

Motion examples

Research topic 137

Distinguish between different type oftouches Self – User – Environment touchProbabilistic model of the self –environment touches Identification of user’s touches as highly

improbable touches due to other reasons

Questions ?

End of part 1

Part 239

Klinokinesis based control

Klinokinesis“if conditions are improving,keep on in the same direction,otherwise try a new direction”
Dusenbery DB, Performance of basic strategies forfollowing gradients in two dimensions,
Journal of Theoretical Biology, 2001

flagella

CCW rotation

straightmovement

flagella
CW rotation

Random directionchangehttp://www.youtube.com/watch?v=EZ5ATNJfuCs

A minimalistic behavioral rulePrevious motor
command“if conditions are improving,keep on in the same direction,otherwise try a new direction”

A new random
motor command

Travel efficiency

Next motorcommand

If conditions
are improving

Low Good Good Low High EHigh density function

Probability(distance traveled toward the goal)

Performance

Performance increase by random perturbations

Perturbation level

deviation from optimal heading[ log(β/α)]

too smalltarget

good

start

targettoo big

starttargetstart

Analysis of the effect of direction
taken

Markov Chain Model= /2optimaldirectionv

Probability distribution:a Fredholm eq. of the II type

=0

=

=-/2

No closed form!

 tradeoff
Long-term behavior

Short-term behavior

Stationary distribution:

Difference equations

Derivative w.r.t v>0 for statescorresponding
to -/2<</2Lowv ()<0 for statescorresponding
to -/2<</2

Lower probabilities
for bad directions
at t

Convergence to the stationary distribution
sped up by increasing v

Shorter time forachieving highprobability of gooddirections

Highv ()Directions probability, t=0--/20Low v=1/64

/2

-

-/20High 
v=1/2

/2



Directions probability, t=1-

-/20
Low 
v=1/64

/2

-

-/20
High 
v=1/2

/2



Directions probability, t=10-

-/20
Low 
v=1/64

/2

-

-/20
High 
v=1/2

/2



Directions probability, t=104-

-/20
Low 
v=1/64

/2

-

-/20
High 
v=1/2

/2

Choosing Medium  (v=1/4)
High  (v=1/2)
Low  (v=1/64)

t=10-



-3/4

-/2-/40

/4/2

3/4

The optimal value for  depends not only on the problem, butalso on the time available for the task
Whatever >0 is sufficient to have a distribution with a peak in
the optimal direction

Features of MBR (Minimalistic Behavioral Rule)

Extremely simple:
 A single parameter  with low sensitivity
 No model required Minimal memory requirements
 A single binary inputWorks with a wide varietyof systems

Nonlinear dynamics

 Systems with non-linearities

 System with delays
 Systems with time-variant dynamics

Works in n-dimensional spaces

Probability of the directions
taken in a 3D spaceNon-linear system
51

Number of particles

f(u)

u2

traveled distanceu1Delays

Traveled distance

Low-pass systemdynamics

s = 10-3
N = 104
Low pass effect ()Dead time
Dead time system dynamics

s = 10-3
N = 104

Traveled distance

Dead time in thesystem dynamics

Dead time (d) [time steps]

High dimensional spaces
54

Traveled distanceSpace dimensions (p)ppApplications – mobile robot reaching

Task: reach a red object
Hardware:- Real World Interface B12(Synchro Drive)- 640×480 Logitech webcam
on an omnidirectional mirror

Navigation

No robot model
required Minimal
computational
requirements
 Limited input
information
required Robustness tonoisy information

Number of red pixels

Advantages

Distance [cm]

Robustness to hardware damageNo model of the robot

4 simulated hardware faults

 The robot can change

during runtime!
Robot: simulated mobile robot with two
independent wheels and an omnidirectional
cameraTask: reach a red hemisphereSensory information: number of red pixels
in the camera image

Change in the sizeof a wheel

Uncontrollability ofa wheel

Change of the
Obscuration of 20%
rotation axis of a wheelof the camera

The robot is able toreach the target in all
the cases An optimal ratio
between the noise
and the signal exists
 This ratio depends on
the hardware andenvironment
conditions


PerformanceResultsηMovement in a parameter space60

Applications – humanoid robot

MBR used to control
the parameters of a CPG
network

Task: maximize the crawling
speedApplications – pneumatic robot arm

Frequency

Hardware:- pnematic robot arm
17 actuators, 7DOFs- 2x FL2-08S2 camera
with EMVL-316 CCTV lensDirections close to the optimal are takenwith the highest probabilitycos()Task: reach 3 virtualtargets

Questions ?

Thank you