Computing Movement Geometry A step in Sensory-Motor Transformations Elizabeth Torres & David Zipser.
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Transcript of Computing Movement Geometry A step in Sensory-Motor Transformations Elizabeth Torres & David Zipser.
![Page 1: Computing Movement Geometry A step in Sensory-Motor Transformations Elizabeth Torres & David Zipser.](https://reader031.fdocuments.us/reader031/viewer/2022012321/56649d405503460f94a197d7/html5/thumbnails/1.jpg)
Computing Movement Geometry
A step in Sensory-Motor Transformations
Elizabeth Torres & David Zipser
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Sensory Input Kinematics Motor output
Postural Path Geometry
Speed ?
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Stuff that’s easy in the Geometric Stage
•Specifying movement paths.
•Dealing with excess degrees of freedom.
•Some constraint satisfaction.
•Some error correction.
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target positionx y
z
Geometric Stage Input -- OutputReaching to Grasp with a Multi-jointed Arm
target orientation, ,α β γ
arm posture
Geometric Stage
Arm postural
Path
Represented as changes in
joint angles
What goes on in
here?
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r ≥0 r = f q1 ,q2 ,K ,qn( )
q1 qn
r
Gradient Descent
−∇r q( ) =−
∂r∂q1
, ∂r∂q2
,K , ∂r∂qn
⎛
⎝⎜⎞
⎠⎟
−∇r q( )
q
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x2
x1
r
r = hand to target distance
x3
x = x1,x2 ,x3( )
x t = x1t,x2
t ,x3t( )
r =
2xi−xi
t( )i=1
i=3
∑
Hand to target distance
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x3
x2
q1
q2
q3
q4 q5
q6
q7
x1
Joint Angles
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Posture in 7D Joint Angle Space
Hand position3D Space
f q( )
q f
1q( )
f
2q( )
f
3q( )
x1
x2
x3 x
r=2
xit−fi q( )( )
i=1
i=3∑
r =2
xit−xi( )
i=1
i=3∑
Hand to target distanceAs function of joint angles
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Gradient Descent for Simulating Hand Translation
Δq=−η∇r xt ,q( )
∇r xt ,q( )
On each time step the change in joint angles is:
qinitial
qfinal
Posture path
x final
x initial
Hand path
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Reconfiguring Joint Angle Space
G =2 1+cos q2( )( ) 1+cos q2( )
1+cos q2( ) 1
⎡
⎣
⎢⎢⎢
⎤
⎦
⎥⎥⎥
Example:
x1
x1
x2
x2 r
r ′q =G⋅q
∇'r xt ,q( ) =G−1 ⋅∇r xt ,q( )
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x1
Orientation Matching
x2
x3
H⎡⎣ ⎤⎦
H⎡⎣ ⎤⎦= f q( )
O -1⎡⎣ ⎤⎦=g vision( )
O -1⎡⎣ ⎤⎦
cos φ( ) =
12
Tr R⎡⎣ ⎤⎦−1
R⎡⎣ ⎤⎦= O−1⎡⎣ ⎤⎦ H⎡⎣ ⎤⎦
R⎡⎣ ⎤⎦
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r = xi
t − fi q( )( )2+α kφ( )
2
i=1
i=3
∑
Distance function for translation and rotation
k A constant chosen so that total distance = total rotation
α Co-articulation parameter
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Experiments
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Fitting G to Experimental Data
Best Worst
G symmetrical and positive-definite
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fastnormal
slow
TARGET
Speed Invariance of Movement Path
One subject, one movement at each speed
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Six subjects, six movements each
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r = xi
t − fi q( )( )2+α kφ( )
2
i=1
i=3
∑
Co-articulation
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Error Correction
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Correcting error with retinal image feedback
x
1−x1
t( ) , x2 −x2t( ) , x3 −x3
t( )( ) =12∇r x,xt( )
Hand -Target Offset
∇r xt ,q( )=∇r x,xt( ) J f q( )( ) J = J acobian( )
Using chain rule
J f q( )( ) =
∇ f1
q( )( )
∇ f2
q( )( )
∇ f3
q( )( )
⎡
⎣
⎢⎢⎢
⎤
⎦
⎥⎥⎥
Retina(s)
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Discussion Break
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r 2 = xi
t − fi q( )( )2+α kφ q( )( )
2
i=1
i=3
∑
∇r2 xt ,q( ) =∇ xi
t − fi q( )( )2
i=1
i=3
∑⎛
⎝⎜⎞
⎠⎟+∇ α kφ q( )( )
2⎛⎝
⎞⎠
The gradient of a sum = the sum of the gradients
Each of these terms can be computed in different brain areas
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Hidden Units
dq
posture target position target orientation
x y
z
, ,α β γ
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Preferred Directions Rotate across External Space
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