Realizing an assembly task through virtual capture
Authors: 
Damien Petit, Ixchel G. Ramirez-Alpizar, Wataru Kamei, Qiming He, Kensuke HaradaAuthors Info & Claims
Damien Petit, Ixchel G. Ramirez-Alpizar, Wataru Kamei, Qiming He, Kensuke HaradaAuthors Info & ClaimsPages 2665 - 2670
Abstract
Modern manufacturing strategy requires the robotic infrastructure to be able to adapt to new products or to accomplish new tasks quickly. In order to respond to this demand, teaching a robot to realize a task by demonstration has regained popularity in recent years, especially for dual-arm or humanoid robots. One of the main issues using this method is to adapt the captured motion from the human demonstration to the robot’s specific kinematics and control. In this paper we present a method where the motion and grasping adaptation is tackled during the capture. We demonstrate the validity of this method with an experiment where a humanoid robot realizes an assembly previously demonstrated by a user wearing a Head Mounted Display (HMD) performing an assembly task in a virtual environment.
References
[1]
M. R. Pedersen, L. Nalpantidis, R. S. Andersen, C. Schou, S. Bøgh, V. Krüger, and O. Madsen, “Robot skills for manufacturing: From concept to industrial deployment,” Robotics and Computer-Integrated Manufacturing, vol. 37, pp. 282–291, 2016.
[2]
A. Billard, S. Calinon, R. Dillmann, and S. Schaal, “Robot programming by demonstration,” in Springer handbook of robotics, pp. 1371–1394, Springer, 2008.
[3]
S. Calinon, Robot programming by demonstration. EPFL Press, 2009.
[4]
T. Lau, S. A. Wolfman, P. Domingos, and D. S. Weld, “Programming by demonstration using version space algebra,” Machine Learning, vol. 53, no. 1-2, pp. 111–156, 2003.
[5]
J. Aleotti, S. Caselli, and M. Reggiani, “Toward programming of assembly tasks by demonstration in virtual environments,” in Robot and Human Interactive Communication, 2003. Proceedings. ROMAN 2003. The 12th IEEE International Workshop on, pp. 309–314, IEEE, 2003.
[6]
J. Aleotti, S. Caselli, and M. Reggiani, “Leveraging on a virtual environment for robot programming by demonstration,” Robotics and Autonomous Systems, vol. 47, no. 2-3, pp. 153–161, 2004.
[7]
D. Petit, I. G. Ramirez-Alpizar, Q. He, and K. Harada, “A virtual capture framework for assembly tasks,” in 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), pp. 1618–1623, IEEE, 2018.
[8]
I. Oikonomidis, N. Kyriazis, and A. A. Argyros, “Full dof tracking of a hand interacting with an object by modeling occlusions and physical constraints,” in 2011 International Conference on Computer Vision, pp. 2088–2095, IEEE, 2011.
[9]
M. Corsini, P. Cignoni, and R. Scopigno, “Efficient and flexible sampling with blue noise properties of triangular meshes,” IEEE transactions on visualization and computer graphics, vol. 18, no. 6, pp. 914–924, 2012.
[10]
K. Harada, N. Yamanobe, W. Wan, K. Nagata, I. G. Ramirez-Alpizar, and T. Tsuji, “Motion-data driven grasp/assembly planner,” Journal of Robotics, Networking and Artificial Life, vol. 5, no. 4, pp. 232–235, 2019.
[11]
S. Garrido-Jurado, R. Muoz-Salinas, F. Madrid-Cuevas, and R. Medina-Carnicer, “Generation of fiducial marker dictionaries using mixed integer linear programming,” Pattern Recognition, vol. 51, 10 2015.
[12]
W. Li and P. Song, “A modified icp algorithm based on dynamic adjustment factor for registration of point cloud and cad model,” Pattern Recognition Letters, vol. 65, pp. 88–94, 2015.
[13]
K. Harada, T. Tsuji, S. Uto, N. Yamanobe, K. Nagata, and K. Kitagaki, “Stability of soft-finger grasp under gravity,” in 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 883–888, IEEE, 2014.
[14]
H. G. Hoffman, “Physically touching virtual objects using tactile augmentation enhances the realism of virtual environments,” in Proceedings. IEEE 1998 Virtual Reality Annual International Symposium (Cat. No. 98CB36180), pp. 59–63, IEEE, 1998.
[15]
S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen, “Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection,” The International Journal of Robotics Research, vol. 37, no. 4-5, pp. 421–436, 2018.
[16]
D. Arumugam, J. K. Lee, S. Saskin, and M. L. Littman, “Deep reinforcement learning from policy-dependent human feedback,” arXiv preprint arXiv:1902.04257, 2019.
Index Terms
- Realizing an assembly task through virtual capture
Index terms have been assigned to the content through auto-classification.
Recommendations
A virtual capture framework for assembly tasks
2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)Nowadays more robots are used in manufacturing to accomplish more complex tasks, especially in assembly. Programming by demonstration method allows to teach robots some specific tasks, but it requires a lot of data. However in assembly, wearable motion ...
Robot learning of industrial assembly task via human demonstrations
Human---robot collaboration in industrial applications is a challenging robotic task. Human working together with the robot at a workplace to complete a task may create unpredicted events for the robot, as humans can act unpredictably. Humans tend to ...
Hand/Arm Robot Teleoperation by Inertial Motion Capture
RVSP '13: Proceedings of the 2013 Second International Conference on Robot, Vision and Signal ProcessingThe multi-fingered robot hand has much attention in various fields. Many robot hands have been proposed so far and we have developed a hand/arm robot with universal robot hand II. A teleoperation system allows intuitive manipulation of the hand/arm ...
Comments
Information & Contributors
Information
Published In
October 2019
4424 pages
Copyright © 2019.
Publisher
IEEE Press
Publication History
Published: 01 October 2019
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 02 Feb 2025