This is the slide used for IEEE International Conference on Robotics and Automation (ICRA) 2017, Workshop on Learning and Control for Autonomous Manipulation Systems on June 2nd, 2017.

1. Hitoshi Kusano*, Ayaka Kume+, Eiichi Matsumoto+, Jethro Tan+
June 2, 2017
*Kyoto University
+Preferred Networks, Inc.
FCN-Based 6D Robotic Grasping
for Arbitrary Placed Objects
※This work is the output of Preferred Networks internship program

2. Requirement for successful robotic grasping:
Derive configurations of a robot and its end-effector
e.g. Grasp pose, Grasp width, Grasp height, Joint angle
・Traditional approach decomposes grasping process into
several stages, which require many heuristics
・Machine learning based end-to-end approach has emerged
Background
http://www.schunk-modular-robotics.com/
1/9
Complex end-effector Cluttered environment

3. None of prior methods can predict 6D grasp
Previous Work
~ Machine learning based end-to-end approach ~
Pinto2016 Levine2016
Araki2016 Guo2017
(x, y)height
width
2/9
(x, y, z, roll, pitch, yaw)

4. Our purpose:
End-to-End learning to grasp arbitrary placed objects
Contribution:
○ Novel data collection strategy to obtain 6D grasp
configurations using a teach tool by human
○ End-to-end CNN model predicting 6D grasp configurations
Purpose and Contribution
(x, y, z, w, p, r)
3/9

5. ● An extension for Fully Convolutional Networks
● Outputs two maps with scores: Location Map for graspability per pixel, and
Configuration Map providing end-effector configurations (z, w, p, r) per pixel
● For Configuration Map, this network classifies valid grasp configurations to
300 classes, NOT regression
Grasp Configuration Network
(x, y, z, w, p, r)
4/9
Location MapConfiguration Map

6. Data Collection
Simple teach tool Data Collection
We demonstrated 11320 grasps for 7 objects
5/9
Robotic Gripper
https://www.thk.com
X

7. A. Intel Realsense SR300 RGB-D camera
B. Arbitrary placed object
C. THK TRX-S 3-finger gripper
D. FANUC M-10iA 6 DOF robot arm
Experiment Setup
B
C
D
A
6/9

8. ● Predicted grasp configurations for the same (X,Y) location
Example of predicted grasp configurations
Cap
Bottle
TOP VIEW FRONT VIEW
Grasp Candidate Grasp Candidate
7/9

9. Known Objects Unknown Objects
Results of robotic experiment
70% 50% 60% 40%
20% 40% 60%
Number under the figure means success rate for 10 trials
60% 20% 20% 40% 30%
8/9
_

10. System Test
※This video is double speed
9/9

11. Thank you for listening
and
I hope to talk to you in the interactive session