A modular approach to learning manipulation strategies from human demonstration
- 816 Downloads
Object manipulation is a challenging task for robotics, as the physics involved in object interaction is complex and hard to express analytically. Here we introduce a modular approach for learning a manipulation strategy from human demonstration. Firstly we record a human performing a task that requires an adaptive control strategy in different conditions, i.e. different task contexts. We then perform modular decomposition of the control strategy, using phases of the recorded actions to guide segmentation. Each module represents a part of the strategy, encoded as a pair of forward and inverse models. All modules contribute to the final control policy; their recommendations are integrated via a system of weighting based on their own estimated error in the current task context. We validate our approach by demonstrating it, both in a simulation for clarity, and on a real robot platform to demonstrate robustness and capacity to generalise. The robot task is opening bottle caps. We show that our approach can modularize an adaptive control strategy and generate appropriate motor commands for the robot to accomplish the complete task, even for novel bottles.
KeywordsLearning by demonstration Manipulation Modular approach
This work was funded primarily by the Swiss National Foundation through the National Center of Competence in Research (NCCR) in Robotics. Ravin de Souza was also supported by a doctoral grant (SFRH/BD/51071/2010) from the Portuguese Fundacao para a Ciencia e a Tecnologia and Miao Li was supported by the European Union Seventh Framework ProgrammeP7/2007–2013 under Grant agreement no 288533 ROBOHOW.COG. Bidan Huang was also supported by a studentship from the University of Bath. The authors would like to thank Sahar El-Khoury for her valuable comments.
- Bernardino, A., Henriques, M., Hendrich, N., & Zhang, J. (2013). Precision grasp synergies for dexterous robotic hands. In IEEE International Conference on Robotics and Biomimetics (ROBIO) (pp. 62–67). IEEE, Piscataway.Google Scholar
- Berndt, D. J., & Clifford, J. (1994). Using dynamic time warping to find patterns in time series. In KDD Workshop, Seattle, WA (vol. 10, pp. 359–370).Google Scholar
- Bryson, J. J., & Stein, L. A. (2001). Modularity and design in reactive intelligence. In Proceedings of the 17th International Joint Conference on Artificial Intelligence (pp. 1115–1120). Seattle: Morgan Kaufmann.Google Scholar
- Calinon, S., & Billard, A. (2007). Incremental learning of gestures by imitation in a humanoid robot. In Proceedings of the ACM/IEEE international conference on Human-robot interaction (pp. 255–262). ACM, New York.Google Scholar
- Cohn, D. A., Ghahramani, Z., & Jordan, M. I. (1996). Active learning with statistical models. arXiv:cs/9603104, preprint.
- Do, M., Asfour, T., & Dillmann, R. (2011). Towards a unifying grasp representation for imitation learning on humanoid robots. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 482–488). IEEE, Piscataway.Google Scholar
- Fischer, M., van der Smagt, P., & Hirzinger, G. (1998) Learning techniques in a dataglove based telemanipulation system for the DLR hand. In Proceedings of 1998 IEEE International Conference on Robotics and Automation (vol. 2, pp 1603–1608). IEEE, Piscataway.Google Scholar
- Gustafsson, E. (2013). Investigation of friction between plastic parts. Master’s thesis, Chalmers University of Technology, Gothenburg.Google Scholar
- Howard, M., Mitrovic, D., & Vijayakumar, S. (2010). Transferring impedance control strategies between heterogeneous systems via apprenticeship learning. In 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids) (pp. 98–105)Google Scholar
- Huang, B., Bryson, J., & Inamura, T. (2013a). Learning Motion Primitives of Object Manipulation Using Mimesis Model. In Proceedings of 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO) Google Scholar
- Huang, B., El-Khoury, S., Li, M., Bryson, J. J., & Billard, A. (2013b). Learning a real time grasping strategy. In 2013 IEEE International Conference on Robotics and Automation (ICRA) (pp. 593–600). IEEE, PiscatawayGoogle Scholar
- Hueser, M., Baier, T., Zhang, J. (2006). Learning of demonstrated grasping skills by stereoscopic tracking of human head configuration. In Proceedings 2006 IEEE International Conference on Robotics and Automation (ICRA 2006) (pp. 2795–2800). IEEE, Piscataway.Google Scholar
- Johnson, M., & Demiris, Y. (2005). Hierarchies of coupled inverse and forward models for abstraction in robot action planning, recognition and imitation. In Proceedings of the AISB 2005 Symposium on Imitation in Animals and Artifacts, Citeseer (pp. 69–76)Google Scholar
- Korkinof, D., & Demiris, Y. (2013). Online quantum mixture regression for trajectory learning by demonstration. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3222–3229). IEEE, Piscataway.Google Scholar
- Kortenkamp, D., Bonasso, R. P., & Murphy, R. (Eds.). (1998). Artificial intelligence and mobile robots: Case studies of successful robot systems. Cambridge, MA: MIT Press.Google Scholar
- Kronander, K., & Billard, A. (2012). Online learning of varying stiffness through physical human-robot interaction. In 2012 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1842–1849). IEEE, Piscataway.Google Scholar
- Li, M., Yin, H., Tahara, K., & Billard, A. (2014). Learning object-level impedance control for robust grasping and dexterous manipulation. In Proceedings of International Conference on Robotics and Automation (ICRA), 2014.Google Scholar
- Nakanishi, J., Radulescu, A., & Vijayakumar, S. (2013). Spatio-temporal optimization of multi-phase movements: Dealing with contacts and switching dynamics. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5100–5107). IEEE, Piscataway.Google Scholar
- Nehaniv, C. L., & Dautenhahn, K. (2002). The correspondence problem, chapter 2. In K. Dautenhahn & C. L. Nehaniv (Eds.), Imitation in animals and artifacts (pp. 41–62). Cambridge: MIT Press.Google Scholar
- Okamura, A. M., Smaby, N., & Cutkosky, M. R. (2000). An overview of dexterous manipulation. In Proceedings of IEEE International Conference on Robotics and Automation (ICRA’00) (vol. 1, pp. 255–262). IEEE, PiscatawayGoogle Scholar
- Pais, AL., & Billard, A. (2014). Encoding bi-manual coordination patterns from human demonstrations. In Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction (pp. 264–265). ACM, New York.Google Scholar
- Pais, L., Umezawa, K., Nakamura, Y., & Billard, A. (2013). Learning robot skills through motion segmentation and constraints extraction. In HRI Workshop on Collaborative Manipulation.Google Scholar
- Pastor, P., Kalakrishnan, M., Chitta, S., Theodorou, E., & Schaal, S. (2011). Skill learning and task outcome prediction for manipulation. In 2011 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3828–3834). IEEE, Piscataway.Google Scholar
- Petkos, G., Toussaint, M., & Vijayakumar, S. (2006). Learning multiple models of non-linear dynamics for control under varying contexts. In Artificial Neural Networks–ICANN 2006 (pp. 898–907). Springer, Berlin.Google Scholar
- de Souza, R., El Khoury, S., Santos-Victor, J., & Billard, A. (2014). Towards comprehensive capture of human grasping and manipulation skills. In 13th International Symposium on 3D Analysis of Human Movement Google Scholar
- Tribology-abccom. (2014). Coefficient of friction, rolling resistance, air resistance, aerodynamics. Retrieved August 09, 2014, from http://www.tribology-abc.com/abc/cof.htm.