deep learning for detecting robotic grasps · de nition deep learning for detecting robotic grasps...
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University of Hamburg
MIN Faculty
Department of Informatics
Deep Learning for Detecting Robotic Grasps
Deep Learning For Detecting Robotic Grasps
Waleed Mustafa
University of HamburgFaculty of Mathematics, Informatics and Natural SciencesDepartment of Informatics
Technical Aspects of Multimodal Systems
4. Januar 2016
W. Mustafa 1
University of Hamburg
MIN Faculty
Department of Informatics
Deep Learning for Detecting Robotic Grasps
Outline
1. Definition
2. Motivation
3. Overview of Grasp Process
4. Grasp Representation
5. Detect Grasp from Image
6. Learning Grasp Ranking Function
7. Resutls
8. Demo
9. Conclusion
10. References
W. Mustafa 2
University of Hamburg
MIN Faculty
Department of Informatics
Definition Deep Learning for Detecting Robotic Grasps
Outline
1. Definition
2. Motivation
3. Overview of Grasp Process
4. Grasp Representation
5. Detect Grasp from Image
6. Learning Grasp Ranking Function
7. Resutls
8. Demo
9. Conclusion
10. References
W. Mustafa 3
University of Hamburg
MIN Faculty
Department of Informatics
Definition Deep Learning for Detecting Robotic Grasps
Definition
Grasp
”A grasp is commonly defined as a set of contacts on the surfaceof the object, which purpose is to constrain the potentialmovements of the object in the event of externaldisturbances”Leon et al. (2014)
Grasp Synthesis
”Grasp synthesis is the problem of finding a suitable set ofcontacts given an object and some constraints on the allowablecontacts”Leon et al. (2014)
W. Mustafa 4
University of Hamburg
MIN Faculty
Department of Informatics
Motivation Deep Learning for Detecting Robotic Grasps
Outline
1. Definition
2. Motivation
3. Overview of Grasp Process
4. Grasp Representation
5. Detect Grasp from Image
6. Learning Grasp Ranking Function
7. Resutls
8. Demo
9. Conclusion
10. References
W. Mustafa 5
University of Hamburg
MIN Faculty
Department of Informatics
Motivation Deep Learning for Detecting Robotic Grasps
MotivationWhy do we need robotic grasp?
I Almost all robotic applications include manipulation of objects
I In order to manipulate an object you need first to grasp itI Applications include:
I Exploration
I Household
I Industry robotic hands
W. Mustafa 6
University of Hamburg
MIN Faculty
Department of Informatics
Overview of Grasp Process Deep Learning for Detecting Robotic Grasps
Outline
1. Definition
2. Motivation
3. Overview of Grasp Process
4. Grasp Representation
5. Detect Grasp from Image
6. Learning Grasp Ranking Function
7. Resutls
8. Demo
9. Conclusion
10. References
W. Mustafa 7
University of Hamburg
MIN Faculty
Department of Informatics
Overview of Grasp Process Deep Learning for Detecting Robotic Grasps
Overview of Grasp Process
Goal:
Predict Gripper configuration (i.e., Gripper Location,Orientation, and Gripper Opening Width)
Input:
2-D Image, and Depth Map
Output:
Grasp Representation? We need parameters that represent thegripper configuration
W. Mustafa 8
University of Hamburg
MIN Faculty
Department of Informatics
Grasp Representation Deep Learning for Detecting Robotic Grasps
Outline
1. Definition
2. Motivation
3. Overview of Grasp Process
4. Grasp Representation
5. Detect Grasp from Image
6. Learning Grasp Ranking Function
7. Resutls
8. Demo
9. Conclusion
10. References
W. Mustafa 9
University of Hamburg
MIN Faculty
Department of Informatics
Grasp Representation Deep Learning for Detecting Robotic Grasps
Grasp Representation
I A Grasp is:I Gripper LocationsI Gripper PoseI Gripper Opening Width
W. Mustafa 10
University of Hamburg
MIN Faculty
Department of Informatics
Grasp Representation Deep Learning for Detecting Robotic Grasps
Grasp Representation (cont.)
I A good representation is:I Easily predicted it from sensory dataI The full grasp parameters can be retrieved from it
I Saxena et al. (2008) proposed one point as a representation ofa graspI Easy to predictI Procedure for retrieving Grasp parameters hard and faulty
I Jiang et al. (2011) Represented grasps as an oriented rectangle
W. Mustafa 11
University of Hamburg
MIN Faculty
Department of Informatics
Grasp Representation Deep Learning for Detecting Robotic Grasps
Grasp Representation (cont.)
I Grasp is defined by:I rG ,cG position in image plan, mG , nG width and Height of
rectangle θ is the angle of rectangle with respect to X -axis
W. Mustafa 12
University of Hamburg
MIN Faculty
Department of Informatics
Grasp Representation Deep Learning for Detecting Robotic Grasps
Grasp Representation
I Positions, Opening Width, and pose around camera axis aredirectly defined
I Other two angles is computed by:I Select the point with lower depth in the middle thirdI Compute average surface norm around this point Jiang et al.
(2011)
W. Mustafa 13
University of Hamburg
MIN Faculty
Department of Informatics
Detect Grasp from Image Deep Learning for Detecting Robotic Grasps
Outline
1. Definition
2. Motivation
3. Overview of Grasp Process
4. Grasp Representation
5. Detect Grasp from Image
6. Learning Grasp Ranking Function
7. Resutls
8. Demo
9. Conclusion
10. References
W. Mustafa 14
University of Hamburg
MIN Faculty
Department of Informatics
Detect Grasp from Image Deep Learning for Detecting Robotic Grasps
Detect Grasp from Image
1. Generate every possible grasp rectangle
W. Mustafa 15
University of Hamburg
MIN Faculty
Department of Informatics
Detect Grasp from Image Deep Learning for Detecting Robotic Grasps
Detect Grasp from Image (cont.)
2. Using a function f (x |Θ) : Rn → [0, 1] rank the rectangles,where x is features computed from rectangles
3. Choose the rectangle with highest rank
W. Mustafa 16
University of Hamburg
MIN Faculty
Department of Informatics
Learning Grasp Ranking Function Deep Learning for Detecting Robotic Grasps
Outline
1. Definition
2. Motivation
3. Overview of Grasp Process
4. Grasp Representation
5. Detect Grasp from Image
6. Learning Grasp Ranking Function
7. Resutls
8. Demo
9. Conclusion
10. References
W. Mustafa 17
University of Hamburg
MIN Faculty
Department of Informatics
Learning Grasp Ranking Function Deep Learning for Detecting Robotic Grasps
Learning Grasp Ranking Function
I Given a training set (Images with Human marking) we cantrain the function f (x |Θ)
I Jiang et al. (2011) proposed extracting feature from potentialrectangle and build an SVM classifierI Their features was histogram of different filters
I Lenz et al. (2015) Used sparse auto-encoder to automaticallylearn features Goodfellow et al. (2009)I Used neural network to learn the rank functionI The input to the network
W. Mustafa 18
University of Hamburg
MIN Faculty
Department of Informatics
Learning Grasp Ranking Function Deep Learning for Detecting Robotic Grasps
W ∗ = arg minW
∑Mt=1 (‖x − x‖2
2 + λ∑K
j=1 g(h(t))) + βf (W )
h(t)j = σ(
∑Ni=1 x
(t)i Wij)
x(t)i =
∑Kj=1 h
(t)j Wij
Lenz et al. (2015)W. Mustafa 19
University of Hamburg
MIN Faculty
Department of Informatics
Learning Grasp Ranking Function Deep Learning for Detecting Robotic Grasps
I We can repeat the above process to learn N layersI Finally we stack learned layers together with an output decision
layersI Complete the learning with BP
Lenz et al. (2015)
W. Mustafa 20
University of Hamburg
MIN Faculty
Department of Informatics
Learning Grasp Ranking Function Deep Learning for Detecting Robotic Grasps
I It is very slow to run the huge network on all the rectangles
I Lenz et al. (2015) propose a cascaded system
Lenz et al. (2015)
W. Mustafa 21
University of Hamburg
MIN Faculty
Department of Informatics
Learning Grasp Ranking Function Deep Learning for Detecting Robotic Grasps
Multi-modal Input
I Concatenate the data from different modes
Lenz et al. (2015)
W. Mustafa 22
University of Hamburg
MIN Faculty
Department of Informatics
Learning Grasp Ranking Function Deep Learning for Detecting Robotic Grasps
Multi-modal Input (cont.)
I Separate modes at the first layer
Lenz et al. (2015)
W. Mustafa 23
University of Hamburg
MIN Faculty
Department of Informatics
Learning Grasp Ranking Function Deep Learning for Detecting Robotic Grasps
Multi-modal Input (cont.)
I Or a mix
Lenz et al. (2015)
W. Mustafa 24
University of Hamburg
MIN Faculty
Department of Informatics
Resutls Deep Learning for Detecting Robotic Grasps
Outline
1. Definition
2. Motivation
3. Overview of Grasp Process
4. Grasp Representation
5. Detect Grasp from Image
6. Learning Grasp Ranking Function
7. Resutls
8. Demo
9. Conclusion
10. References
W. Mustafa 25
University of Hamburg
MIN Faculty
Department of Informatics
Resutls Deep Learning for Detecting Robotic Grasps
Resutls
Lenz et al. (2015)
W. Mustafa 26
University of Hamburg
MIN Faculty
Department of Informatics
Demo Deep Learning for Detecting Robotic Grasps
Outline
1. Definition
2. Motivation
3. Overview of Grasp Process
4. Grasp Representation
5. Detect Grasp from Image
6. Learning Grasp Ranking Function
7. Resutls
8. Demo
9. Conclusion
10. References
W. Mustafa 27
University of Hamburg
MIN Faculty
Department of Informatics
Demo Deep Learning for Detecting Robotic Grasps
W. Mustafa 28
University of Hamburg
MIN Faculty
Department of Informatics
Conclusion Deep Learning for Detecting Robotic Grasps
Outline
1. Definition
2. Motivation
3. Overview of Grasp Process
4. Grasp Representation
5. Detect Grasp from Image
6. Learning Grasp Ranking Function
7. Resutls
8. Demo
9. Conclusion
10. References
W. Mustafa 29
University of Hamburg
MIN Faculty
Department of Informatics
Conclusion Deep Learning for Detecting Robotic Grasps
Conclusion
I We introduced a grasp detection system based on deep learning
I Results shows that it outperforms systems that are based onhuman designed features
I The problem have a lot on common with object detection
I A lot of methods in object detection can be used
I Overfeat will be fasterI CNN might be more suitable for Images
W. Mustafa 30
University of Hamburg
MIN Faculty
Department of Informatics
References Deep Learning for Detecting Robotic Grasps
Outline
1. Definition
2. Motivation
3. Overview of Grasp Process
4. Grasp Representation
5. Detect Grasp from Image
6. Learning Grasp Ranking Function
7. Resutls
8. Demo
9. Conclusion
10. References
W. Mustafa 31
University of Hamburg
MIN Faculty
Department of Informatics
References Deep Learning for Detecting Robotic Grasps
References
I Goodfellow, H Lee, and QV Le. Measuring invariances in deepnetworks. Advances in neural . . . , 2009. URLhttp://papers.nips.cc/paper/
3790-measuring-invariances-in-deep-networks.
Y Jiang, S Moseson, and A Saxena. Efficient grasping from rgbdimages: Learning using a new rectangle representation. Roboticsand Automation ( . . . , 2011. URL http://ieeexplore.ieee.
org/xpls/abs_all.jsp?arnumber=5980145.
I Lenz, H Lee, and A Saxena. Deep learning for detecting roboticgrasps. The International Journal of Robotics, 2015. URLhttp://ijr.sagepub.com/content/34/4-5/705.short.
W. Mustafa 32
University of Hamburg
MIN Faculty
Department of Informatics
References Deep Learning for Detecting Robotic Grasps
References (cont.)
B Leon, A Morales, and J Sancho-Bru. From robot to humangrasping simulation. 2014. URL http://link.springer.com/
content/pdf/10.1007/978-3-319-01833-1.pdf.
A Saxena, J Driemeyer, and AY Ng. Robotic grasping of novelobjects using vision. International Journal of Robotics, 2008.URL http://ijr.sagepub.com/content/27/2/157.short.
W. Mustafa 33