Matthew Piccoli University of PennsylvaniaClosed Loop Grasping Initial goals: Grasp delicate objects...

Matthew Piccoli

University of Pennsylvania

Base Controller

Lots of deprecated code

Separate odometry from controller

Use common baseKinematics class

Continuously updated throughout the summer

Choices…

Safe teleop (base)

Velocity control

Goal control

Closed loop grasping

Using fingertip sensors

Cart pushing/trailer pulling

Safe Teleop

Two methods:

Velocity control

○ The path of the commanded velocity from the joystick

is projected forward

○ If path crosses obstacle, linearly decrease speed with

distance

Goal control

○ The location of the goal is controlled by the joystick

○ Move_base or move_base_local plans to that goal

Closed Loop Grasping

Initial goals:

Identify objects

Grasp delicate objects (like eggs)


Initial goals:

Identify objects


Squishy ball Wood block

Impulse from motor momentum!


Initial goals:


Identify objects

Need to prevent force spike

Force spike from impulse

Need to reduce impulse

Impulse from momentum

Need to reduce momentum

Momentum from velocity (motors/materials)

Need to reduce velocity


Initial goals:


Identify objects

Velocity controller Will attempt to go through object at specified velocity

Need to switch to a controller that won’t go through the

object, but will hold onto it

○ Effort controller

Need to switch at impact with object (on both fingertips)

Can use fingertip sensors or change in current to motor


Initial goals:


Identify objects

Open loop Closed loop


Initial goals:


Identify objects

Things we can get from the controller:

First contact location

Peak force location

Steady state location

Peak force

Steady state force

Classification Output (Weka)

Odwalla vs Naked vs Can vs WaterJ48 pruned tree

------------------

first_contact_distance <= 0.059488| first_contact_distance <= 0.057874: naked (38.0)| first_contact_distance > 0.057874| | df/dt <= 17278.83919: naked (4.0)| | df/dt > 17278.83919| | | peak_force_distance <= 0.051701| | | | first_contact_distance <= 0.058224: naked (4.0)| | | | first_contact_distance > 0.058224: odwalla (19.0/2.0)| | | peak_force_distance > 0.051701: odwalla (43.0)first_contact_distance > 0.059488| fingertip_peak_force_right <= 9437: water (70.0/1.0)| fingertip_peak_force_right > 9437| | dx/dt <= 0.008679: can (42.0)| | dx/dt > 0.008679: water (9.0/1.0)

Number of Leaves : 8

Size of the tree : 15

Time taken to build model: 0.01 seconds

=== Stratified cross-validation ====== Summary ===

Correctly Classified Instances 215 93.8865 %Incorrectly Classified Instances 14 6.1135 %Kappa statistic 0.9167Mean absolute error 0.0378Root mean squared error 0.1729Relative absolute error 10.2468 %Root relative squared error 40.2635 %Total Number of Instances 229

=== Detailed Accuracy By Class ===

TP Rate FP Rate Precision Recall F-Measure ROC Area Class0.967 0.047 0.879 0.967 0.921 0.963 odwalla0.833 0.006 0.976 0.833 0.899 0.946 naked0.932 0.005 0.976 0.932 0.953 0.962 can0.987 0.026 0.95 0.987 0.968 0.972 water

Weighted Avg. 0.939 0.023 0.942 0.939 0.938 0.962

=== Confusion Matrix ===

a b c d <-- classified as58 1 0 1 | a = odwalla8 40 0 0 | b = naked0 0 41 3 | c = can0 0 1 76 | d = water

93.8865 %

a b c d <-- classified as

58 1 0 1 | a = odwalla

8 40 0 0 | b = naked

0 0 41 3 | c = can

0 0 1 76 | d = water


Goals:


Identify objects

Identify object states

Identify fruit ripeness


Odwalla vs Naked vs Can vs Water

and Open vs Closed and Full vs EmptyJ48 pruned tree

------------------=== Stratified cross-validation ====== Summary ===



TP Rate FP Rate Precision Recall F-Measure ROC Area Class1 0.005 0.938 1 0.968 0.998 odwallafullclosed0.733 0.033 0.611 0.733 0.667 0.855 odwallafullopen0.6 0.014 0.75 0.6 0.667 0.885 odwallaemptyopen0.643 0.019 0.692 0.643 0.667 0.813 nakedfullclosed0.286 0.023 0.444 0.286 0.348 0.689 nakedfullopen0.7 0.037 0.467 0.7 0.56 0.925 nakedemptyclosed0.6 0.018 0.6 0.6 0.6 0.791 nakedemptyopen0.467 0.033 0.5 0.467 0.483 0.78 odwallaemptyclosed1 0.005 0.933 1 0.966 0.998 canfullclosed0.6 0.028 0.6 0.6 0.6 0.847 canfullopen0.467 0.042 0.438 0.467 0.452 0.767 canemptyopen0.278 0.062 0.278 0.278 0.278 0.641 waterfullclosed0.158 0.086 0.143 0.158 0.15 0.709 waterfullopen0.15 0.038 0.273 0.15 0.194 0.597 wateremptyopen0.35 0.091 0.269 0.35 0.304 0.704 wateremptyclosed

Weighted Avg. 0.507 0.039 0.505 0.507 0.501 0.787


a b c d e f g h i j k l m n o <-- classified as15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | a = odwallafullclosed0 11 0 0 0 0 0 3 0 0 0 1 0 0 0 | b = odwallafullopen0 0 9 0 0 3 1 2 0 0 0 0 0 0 0 | c = odwallaemptyopen1 2 0 9 2 0 0 0 0 0 0 0 0 0 0 | d = nakedfullclosed0 0 2 4 4 1 1 2 0 0 0 0 0 0 0 | e = nakedfullopen0 0 0 0 1 7 2 0 0 0 0 0 0 0 0 | f = nakedemptyclosed0 0 0 0 1 3 6 0 0 0 0 0 0 0 0 | g = nakedemptyopen0 5 1 0 1 1 0 7 0 0 0 0 0 0 0 | h = odwallaemptyclosed0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 | i = canfullclosed0 0 0 0 0 0 0 0 0 9 6 0 0 0 0 | j = canfullopen0 0 0 0 0 0 0 0 0 4 7 2 1 0 1 | k = canemptyopen0 0 0 0 0 0 0 0 1 2 0 5 4 1 5 | l = waterfullclosed0 0 0 0 0 0 0 0 0 0 1 5 3 4 6 | m = waterfullopen0 0 0 0 0 0 0 0 0 0 0 1 9 3 7 | n = wateremptyopen0 0 0 0 0 0 0 0 0 0 2 4 4 3 7 | o = wateremptyclosed

50.655 %

0 0 0 0 0 0 0 0 1 2 0 5 4 1 5 | l = waterfullclosed

0 0 0 0 0 0 0 0 0 0 1 5 3 4 6 | m = waterfullopen

0 0 0 0 0 0 0 0 0 0 0 1 9 3 7 | n = wateremptyopen

0 0 0 0 0 0 0 0 0 0 2 4 4 3 7 | o = wateremptyclosed


Odwalla vs Naked vs Can and Open

vs Closed and Full vs EmptyJ48 pruned tree




TP Rate FP Rate Precision Recall F-Measure ROC Area Class0.933 0.022 0.824 0.933 0.875 0.958 odwallafullclosed0.667 0.044 0.625 0.667 0.645 0.846 odwallafullopen0.667 0.058 0.556 0.667 0.606 0.852 odwallaemptyopen0.5 0.029 0.636 0.5 0.56 0.76 nakedfullclosed0.429 0.036 0.545 0.429 0.48 0.705 nakedfullopen0.6 0.028 0.6 0.6 0.6 0.925 nakedemptyclosed0.7 0.014 0.778 0.7 0.737 0.842 nakedemptyopen0.467 0.058 0.467 0.467 0.467 0.702 odwallaemptyclosed0.929 0 1 0.929 0.963 0.964 canfullclosed0.667 0.051 0.588 0.667 0.625 0.809 canfullopen0.6 0.044 0.6 0.6 0.6 0.926 canemptyopen

Weighted Avg. 0.651 0.036 0.653 0.651 0.649 0.843


a b c d e f g h i j k <-- classified as14 1 0 0 0 0 0 0 0 0 0 | a = odwallafullclosed0 10 0 0 0 0 0 4 0 0 1 | b = odwallafullopen0 0 10 0 0 2 0 3 0 0 0 | c = odwallaemptyopen3 0 0 7 3 0 0 1 0 0 0 | d = nakedfullclosed0 1 4 3 6 0 0 0 0 0 0 | e = nakedfullopen0 0 1 0 1 6 2 0 0 0 0 | f = nakedemptyclosed0 0 0 0 1 2 7 0 0 0 0 | g = nakedemptyopen0 4 3 1 0 0 0 7 0 0 0 | h = odwallaemptyclosed0 0 0 0 0 0 0 0 13 1 0 | i = canfullclosed0 0 0 0 0 0 0 0 0 10 5 | j = canfullopen0 0 0 0 0 0 0 0 0 6 9 | k = canemptyopen

65.1316 %

Without Water


Open vs Closed and Full vs EmptyJ48 pruned tree




TP Rate FP Rate Precision Recall F-Measure ROC Area Class1 0.015 0.882 1 0.938 0.993 odwallafullclosed0.8 0.007 0.923 0.8 0.857 0.928 odwallafullopen0.8 0.044 0.667 0.8 0.727 0.911 odwallaemptyopen0.857 0.043 0.667 0.857 0.75 0.936 nakedfullclosed0.714 0.014 0.833 0.714 0.769 0.915 nakedfullopen0.7 0.007 0.875 0.7 0.778 0.946 nakedemptyclosed0.8 0.014 0.8 0.8 0.8 0.946 nakedemptyopen0.467 0.036 0.583 0.467 0.519 0.835 odwallaemptyclosed0.929 0 1 0.929 0.963 0.964 canfullclosed0.667 0.051 0.588 0.667 0.625 0.809 canfullopen0.6 0.036 0.643 0.6 0.621 0.931 canemptyopen

Weighted Avg. 0.757 0.025 0.763 0.757 0.755 0.917



75.6579 %

Without Water and Knowing Object


Open vs Closed and Full vs EmptyJ48 pruned tree


Correctly Classified Instances 45 75 %Incorrectly Classified Instances 15 25 %Kappa statistic 0.6667Mean absolute error 0.0468Root mean squared error 0.2024Relative absolute error 33.0524 %Root relative squared error 77.0029 %Total Number of Instances 60


TP Rate FP Rate Precision Recall F-Measure ROC Area Class1 0.044 0.882 1 0.938 0.978 odwallafullclosed0.667 0.044 0.833 0.667 0.741 0.896 odwallafullopen0.733 0.089 0.733 0.733 0.733 0.852 odwallaemptyopen0 0 0 0 0 ? nakedfullclosed0 0 0 0 0 ? nakedfullopen0 0 0 0 0 ? nakedemptyclosed0 0 0 0 0 ? nakedemptyopen0.6 0.156 0.563 0.6 0.581 0.81 odwallaemptyclosed0 0 0 0 0 ? canfullclosed0 0 0 0 0 ? canfullopen0 0 0 0 0 ? canemptyopen

Weighted Avg. 0.75 0.083 0.753 0.75 0.748 0.884



75 %

Odwalla Only

We Can Do Better

Modify the controller to:

Give time to velocity = 0

Repeat grasp with different forces

Give time to location of lowest force trial

velocity = 0 when using larger forces

Give time to spring back to first contact

distance


Open vs Closed and Full vs Empty

With Improved AlgorithmJ48 pruned tree

------------------

time3 <= 0.542| peak_force03 <= 14609| | distance_steady3 <= 0.049578: emptyopen (19.0)| | distance_steady3 > 0.049578| | | steady_force03 <= 9386: emptyopen (4.0)| | | steady_force03 > 9386: emptyclosed (25.0/1.0)| peak_force03 > 14609: fullclosed (24.0)time3 > 0.542: fullopen (24.0)

Number of Leaves : 5

Size of the tree : 9

Time taken to build model: 0 seconds

=== Stratified cross-validation ====== Summary ===



TP Rate FP Rate Precision Recall F-Measure ROC Area Class1 0.014 0.96 1 0.98 0.993 fullclosed0.833 0.028 0.909 0.833 0.87 0.912 emptyopen1 0.014 0.96 1 0.98 0.993 fullopen0.875 0.042 0.875 0.875 0.875 0.929 emptyclosed

Weighted Avg. 0.927 0.024 0.926 0.927 0.926 0.957


a b c d <-- classified as24 0 0 0 | a = fullclosed0 20 1 3 | b = emptyopen0 0 24 0 | c = fullopen1 2 0 21 | d = emptyclosed

92.7083 %

Odwalla Only


Goals:


Identify objects

Identify object states


And that’s as far as we’ve gone

Todo:


Human study

Matthew Piccoli University of PennsylvaniaClosed Loop Grasping Initial goals: Grasp delicate objects...

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