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On transferability and contexts when using simulated grasp databases

Published online by Cambridge University Press:  22 May 2014

Jimmy A. Rytz ,
Lars-Peter Ellekilde ,
Dirk Kraft ,
Henrik G. Petersen  and
Norbert Krüger
Jimmy A. Rytz*
Affiliation:
University of Southern Denmark, Maersk MC-Kinney Møller Institute
Lars-Peter Ellekilde
Affiliation:
University of Southern Denmark, Maersk MC-Kinney Møller Institute
Dirk Kraft
Affiliation:
Syddansk Universitet, Mrsk Mc-Kinney Mller Instituttet
Henrik G. Petersen
Affiliation:
University of Southern Denmark, Maersk MC-Kinney Møller Institute
Norbert Krüger
Affiliation:
University of Southern Denmark, Maersk MC-Kinney Møller Institute
*
*Corresponding author. E-mail: jimali@mmmi.sdu.dk, jimmy.alison.jorgensen@gmail.com
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Summary

It has become a common practice to use simulation to generate large databases of good grasps for grasp planning in robotics research. However, the existence of a generic simulation context that enables the generation of high quality grasps that can be used in several different contexts such as bin-picking or picking objects from a table, has to our knowledge not yet been discussed in the literature.

In this paper, we investigate how well the quality of grasps simulated in a commonly used “generic” context transfers to a specific context, both, in simulation and in the real world.

We generate a large database of grasp hypotheses for several objects and grippers, which we then evaluate in different dynamic simulation contexts e.g., free floating (no gravity, no obstacles), standing on a table and lying on a table.

We present a comparison on the intersection of the grasp outcome space across the different contexts and quantitatively show that to generate reliable grasp databases, it is important to use context specific simulation.

We furthermore evaluate how well a state of the art grasp database transfers from two simulated contexts to a real world context of picking an object from a table and discuss how to evaluate transferability into non-deterministic real world contexts.

Keywords

AutomationGraspingRobot dynamicsControl of robotic systemsPose estimation and registration
Type
Articles
Information
Robotica , Volume 33 , Special Issue 5: Robotics in the Alpe-Adria-Danube Region (RAAD 2013) , June 2015 , pp. 1131 - 1146
Copyright
Copyright © Cambridge University Press 2014 

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References

1. Morales, A., Asfour, T., Azad, P., Knoop, S. and Dillmann, R., “Integrated Grasp Planning and Visual Object Localization for a Humanoid Robot with Five-Fingered Hands,” 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (Oct. 2006) pp. 5663–5668.Google Scholar
2. Berenson, D., Diankov, R., Nishiwaki, K., Kagami, S. and Kuffner, J., “Grasp Planning in Complex Scenes,” 2007 7th IEEE-RAS International Conference on Humanoid Robots (29 2007–dec. 1 2007) pp. 42–48.CrossRefGoogle Scholar
3. Goldfeder, C., Ciocarlie, M., Peretzman, J., Dang, H. and Allen, P. K., “Data-Driven Grasping with Partial Sensor Data,” IROS 2009. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009 (Oct. 2009) pp. 1278–1283.CrossRefGoogle Scholar
4. Goldfeder, C., Ciocarlie, M. T., Dang, H. and Allen, P. K., “The Columbia Grasp Database,” ICRA, IEEE (2009) pp. 1710–1716.Google Scholar
5. Goldfeder, C. and Allen, P. K., “Data-driven grasping,” Auton. Robots 31 (1), 1–20 (2011).Google Scholar
6. Kim, J., Iwamoto, K., Kuffner, J. J., Ota, Y., and Pollard, N. S., “Physically-Based Grasp Quality Evaluation Under Uncertainty,” 2012 IEEE International Conference on Robotics and Automation (ICRA) (2012) pp. 3258–3263.Google Scholar
7. Weisz, J. and Allen, P. K., “Pose error robust grasping from contact wrench space metrics,” ICRA, IEEE (2012) pp. 557–562.Google Scholar
8. Kruger, H., Rimon, E. and van der Stappen, A. F., “Local Force Closure,” 2012 IEEE International Conference on Robotics and Automation (ICRA) (May 2012) pp. 4176 –4182.Google Scholar
9. Detry, R., Kraft, D., Kroemer, O., Bodenhagen, L., Peters, J., Krüger, N. and Piater, J., “Learning grasp affordance densities,” Paladyn 2 (1), 1–17 (June 2011).Google Scholar
10. Wren, D. and Fisher, R. B., “Planning dextrous hand precision grasps from range data, using preshaping and finger trajectories,” Proceedings of the Symposium Intelligent Robotic Systems (SIRS95) (1995).Google Scholar
11. Huebner, K., Ruthotto, S. and Kragic, D., “Minimum Volume Bounding Box Decomposition for Shape Approximation in Robot Grasping,” IEEE International Conference on Robotics and Automation, 2008. ICRA 2008 (2008) pp. 1628–1633.Google Scholar
12. Miller, A. T., Knoop, S., Christensen, H. I. and Allen, P. K., “Automatic Grasp Planning Using Shape Primitives,” Proceedings of the ICRA'03. IEEE International Conference on Robotics and Automation, 2003, vol. 2 (2003), pp. 1824–1829.Google Scholar
13. Ferrari, C. and Canny, J., “Planning Optimal Grasps,” Proceedings of the 1992 IEEE International Conference on Robotics and Automation, 1992, vol. 3 (May 1992) pp. 2290–2295.Google Scholar
14. Kootstra, G., Popovi, M., Jørgensen, J. A., Kragic, D., Petersen, H. G. and Krüger, N., “VisGraB: A benchmark for vision-based grasping,” Paladyn 3 (2), 54–62 (2012).Google Scholar
15. Kraft, D., Ellekilde, L.-P. and Jørgensen, J. A., “Automatic Grasp Generation and Improvement for Industrial Bin-Picking,” In: Gearing Up and Accelerating Crossfertilization between Academic and Industrial Robotics Research in Europe:, volume 94 of Springer Tracts in Advanced Robotics (Röhrbein, F., Veiga, G. and Natale, C., eds.) (Springer International Publishing, 2014) pp. 155–176.Google Scholar
16. Jørgensen, J. A. and Petersen, H. G., “Grasp Synthesis for Dextrous Hands Optimised for Tactile Manipulation,” Proceedings for the Joint Conference of ISR 2010 (41st Internationel Symposium on Robotics) und ROBOTIK 2010 (6th German Conference on Robotics), VDE-Verlag (June 2010).Google Scholar
17. Suárez, R., Roa, M. and Cornella, J., Grasp Quality Measures, Technical Report (Technical University of Catalonia, 2006).Google Scholar
18. Pelossof, R., Miller, A., Allen, P. and Jebara, T., “An Svm Learning Approach to Robotic Grasping,” Proceedings of the ICRA '04. 2004 IEEE International Conference on Robotics and Automation, 2004, vol. 4 (26-May 1, 2004), pp. 3512–3518.Google Scholar
19. Curtis, N. and Xiao, J., “Efficient and Effective Grasping of Novel Objects through Learning and Adapting a Knowledge Base,” IROS 2008. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008 (Sep. 2008) pp. 2252–2257.Google Scholar
20. Goldfeder, C., Ciocarlie, M., Dang, H. and Allen, P. K., “The Columbia Grasp Database,” ICRA '09. IEEE International Conference on Robotics and Automation, 2009 (2009) pp. 1710–1716.Google Scholar
21. Jørgensen, J. A. and Petersen, H., “Usage of Simulations to Plan Stable Grasping of Unknown Objects with a 3-Fingered Schunk Hand,” Workshop on Robot Simulators (IROS08) (2008).Google Scholar
22. Morales, A., Prats, M., Sanz, P. and Pobil, A. P., “An Experiment in the use of Manipulation Primitives and Tactile Perception for Reactive Grasping,” Robotics: Science and Systems, Workshop on Robot Manipulation: Sensing and Adapting to the Real World, Atlanta, USA (2007).Google Scholar
23. Hsiao, K., Chitta, S., Ciocarlie, M. and Jones, E. G., “Contact-Reactive Grasping of Objects with Partial Shape Information,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 10 (2010) pp. 2010.Google Scholar
24. Jørgensen, J. A., Ellekilde, L. P. and Petersen, H. G., “Robworksim - An Open Simulator for Sensor Based Grasping,” Proceedings of Joint 41st International Symposium on Robotics (ISR 2010) and the 6th German Conference on Robotics, Munich (2010).Google Scholar
25. Jørgensen, J. A., Ellekilde, L. P., Kraft, D., Petersen, H. G. and Krüger, N., “On Transferability of Grasp-Affordances in Data-Driven Grasping,” Proceedings of the RAAD 2013 22nd International Workshop on Robotics in Alpe-Adria-Danube Region (2013).Google Scholar
26. Matthews, B. W., “Comparison of the predicted and observed secondary structure of t4 phage lysozyme,” Biochimica et Biophysica Acta (BBA) - Protein Structure 405 (2), 442–451 (1975).Google Scholar
27. Kasper, A., Xue, Z. and Dillmann, R., “The KIT Object Models Database: An Object Model Database for Object Recognition, Localization and Manipulation in Service Robotics,” Int. J. Robot. Res. 31 (8), 927–934 (July 2012).Google Scholar