Matthew Piccoli University of Pennsylvania

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1 Matthew Piccoli University of Pennsylvania

2 Base Controller Lots of deprecated code Separate odometry from controller Use common basekinematics class Continuously updated throughout the summer

3 Choices Safe teleop (base) Velocity control Goal control Closed loop grasping Using fingertip sensors Cart pushing/trailer pulling

4 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

5 Closed Loop Grasping Initial goals: Identify objects Grasp delicate objects (like eggs)

6 Closed Loop Grasping Initial goals: Identify objects Grasp delicate objects (like eggs) Squishy ball Wood block

7 Impulse from motor momentum!

8 Closed Loop Grasping Initial goals: Grasp delicate objects (like eggs) 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

9 Closed Loop Grasping Initial goals: Grasp delicate objects (like eggs) 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

10 Closed Loop Grasping Open loop Initial goals: Grasp delicate objects (like eggs) Identify objects Closed loop

11 Closed Loop Grasping Initial goals: Grasp delicate objects (like eggs) Identify objects Things we can get from the controller: First contact location Peak force location Steady state location Peak force Steady state force

12 Classification Output (Weka) Odwalla vs Naked vs Can vs Water J48 pruned tree first_contact_distance <= first_contact_distance <= : naked (38.0) first_contact_distance > df/dt <= : naked (4.0) df/dt > peak_force_distance <= first_contact_distance <= : naked (4.0) first_contact_distance > : odwalla (19.0/2.0) peak_force_distance > : odwalla (43.0) first_contact_distance > fingertip_peak_force_right <= 9437: water (70.0/1.0) fingertip_peak_force_right > 9437 dx/dt <= : can (42.0) dx/dt > : 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 % Incorrectly Classified Instances % Kappa statistic Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances 229 === Detailed Accuracy By Class === TP Rate FP Rate Precision Recall F-Measure ROC Area Class odwalla naked can water Weighted Avg === Confusion Matrix === a b c d <-- classified as a = odwalla b = naked c = can d = water % a b c d <-- classified as a = odwalla b = naked c = can d = water

13 Closed Loop Grasping Goals: Grasp delicate objects (like eggs) Identify objects Identify object states Identify fruit ripeness

14 Classification Output (Weka) Odwalla vs Naked vs Can vs Water and Open vs Closed and Full vs Empty J48 pruned tree === Stratified cross-validation === === Summary === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances 229 === Detailed Accuracy By Class === TP Rate FP Rate Precision Recall F-Measure ROC Area Class odwallafullclosed odwallafullopen odwallaemptyopen nakedfullclosed nakedfullopen nakedemptyclosed nakedemptyopen odwallaemptyclosed canfullclosed canfullopen canemptyopen waterfullclosed waterfullopen wateremptyopen wateremptyclosed Weighted Avg === Confusion Matrix === a b c d e f g h i j k l m n o <-- classified as a = odwallafullclosed b = odwallafullopen c = odwallaemptyopen d = nakedfullclosed e = nakedfullopen f = nakedemptyclosed g = nakedemptyopen h = odwallaemptyclosed i = canfullclosed j = canfullopen k = canemptyopen l = waterfullclosed m = waterfullopen n = wateremptyopen o = wateremptyclosed % l = waterfullclosed m = waterfullopen n = wateremptyopen o = wateremptyclosed

15 Classification Output (Weka) Odwalla vs Naked vs Can and Open J48 pruned treevs Closed and Full vs Empty === Stratified cross-validation === === Summary === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances 152 === Detailed Accuracy By Class === TP Rate FP Rate Precision Recall F-Measure ROC Area Class odwallafullclosed odwallafullopen odwallaemptyopen nakedfullclosed nakedfullopen nakedemptyclosed nakedemptyopen odwallaemptyclosed canfullclosed canfullopen canemptyopen Weighted Avg === Confusion Matrix === a b c d e f g h i j k <-- classified as a = odwallafullclosed b = odwallafullopen c = odwallaemptyopen d = nakedfullclosed e = nakedfullopen f = nakedemptyclosed g = nakedemptyopen h = odwallaemptyclosed i = canfullclosed j = canfullopen k = canemptyopen % Without Water

16 Classification Output (Weka) Open vs Closed and Full vs Empty J48 pruned tree === Stratified cross-validation === === Summary === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances 152 === Detailed Accuracy By Class === TP Rate FP Rate Precision Recall F-Measure ROC Area Class odwallafullclosed odwallafullopen odwallaemptyopen nakedfullclosed nakedfullopen nakedemptyclosed nakedemptyopen odwallaemptyclosed canfullclosed canfullopen canemptyopen Weighted Avg === Confusion Matrix === a b c d e f g h i j k <-- classified as a = odwallafullclosed b = odwallafullopen c = odwallaemptyopen d = nakedfullclosed e = nakedfullopen f = nakedemptyclosed g = nakedemptyopen h = odwallaemptyclosed i = canfullclosed j = canfullopen k = canemptyopen % Without Water and Knowing Object

17 Classification Output (Weka) Open vs Closed and Full vs Empty J48 pruned tree === Stratified cross-validation === === Summary === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances 60 === Detailed Accuracy By Class === TP Rate FP Rate Precision Recall F-Measure ROC Area Class odwallafullclosed odwallafullopen odwallaemptyopen ? nakedfullclosed ? nakedfullopen ? nakedemptyclosed ? nakedemptyopen odwallaemptyclosed ? canfullclosed ? canfullopen ? canemptyopen Weighted Avg === Confusion Matrix === a b c d e f g h i j k <-- classified as a = odwallafullclosed b = odwallafullopen c = odwallaemptyopen d = nakedfullclosed e = nakedfullopen f = nakedemptyclosed g = nakedemptyopen h = odwallaemptyclosed i = canfullclosed j = canfullopen k = canemptyopen 75 % Odwalla Only

18 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

19 Classification Output (Weka) Open vs Closed and Full vs Empty J48 pruned tree With Improved Algorithm time3 <= peak_force03 <= distance_steady3 <= : emptyopen (19.0) distance_steady3 > 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 === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances 96 === Detailed Accuracy By Class === TP Rate FP Rate Precision Recall F-Measure ROC Area Class fullclosed emptyopen fullopen emptyclosed Weighted Avg === Confusion Matrix === a b c d <-- classified as a = fullclosed b = emptyopen c = fullopen d = emptyclosed % Odwalla Only

20 Closed Loop Grasping Goals: Grasp delicate objects (like eggs) Identify objects Identify object states Identify fruit ripeness And that s as far as we ve gone Todo: Identify fruit ripeness Human study

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