Abstract
In this chapter we propose a framework for easily initializing ProMPs with synthetic data, and building a conditioning dataset in order to map context variables to motion parameters, which can be used for both exploiting its features by executing such contextualized trajectories, or improving through contextual PS. This method is combined with a stochastic-based obstacle avoidance that allows to modify these trajectories. Moreover, this chapter presents generative mixture models of ProMPs, which can be built from data that may include different actions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdolmaleki, A., Simões, D., Lau, N., Reis, L.P., Neumann, G.: Contextual relative entropy policy search with covariance matrix adaptation. In: 2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 94–99 (2016)
Bishop, C.: Pattern Recognition and Machine Learning. Oxford Pergamon Press (1967)
Calinon, S.: A tutorial on task-parameterized movement learning and retrieval. Intell. Serv. Robot. 9(1), 1–29 (2016)
Calinon, S., D’halluin, F., Sauser, E.L., Caldwell, D.G., Billard, A.G.: Learning and reproduction of gestures by imitation. IEEE Robot. Autom. Mag. 17(2), 44–54 (2010)
Colomé, A., Neumann, G., Peters, J., Torras, C.: Dimensionality reduction for probabilistic movement primitives. In: IEEE-RAS 14th International Conference on Humanoid Robots (Humanoids), pp. 794–800 (2014)
Deisenroth, M.P., Neumann, G., Peters, J.: A survey on policy search for robotics. Found. Trends Robot. 2(1–2), 1–142 (2013)
Edelsbrunner, H., Harer, J.L.: Computational Topology: An Introduction. American Mathematical Society (2009)
Fabisch, A., Metzen, J.H.: Active contextual policy search. J. Mach. Learn. Res. 15, 3371–3399 (2014)
Ijspeert, A.J., Nakanishi, J., Hoffmann, H., Pastor, P., Schaal, S.: Dynamical movement primitives: learning attractor models for motor behaviors. Neural Comput. 25(2), 328–373 (2013)
Khansari-Zadeh, S.M., Billard, A.: Learning stable nonlinear dynamical systems with Gaussian mixture models. IEEE Trans. Robot. 27(5), 943–957 (2011)
Koert, D., Maedam, G., Lioutikov, R., Neumann, G., Peters, J.: Demonstration based trajectory optimization for generalizable robot motions. In: IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 515–522 (2016)
Kupcsik, A.G., Deisenroth, M.P., Peters, J., Neumann, G.: Data-efficient generalization of robot skills with contextual policy search. In: AAAI Conference on Artificial Intelligence, pp. 1401–1407 (2013)
Lofberg, J.: YALMIP matlab library. https://yalmip.github.io/download/
Paraschos, A., Daniel, C., Peters, J., Neumann, G.: Probabilistic movement primitives. In: Advances in Neural Information Processing Systems (NIPS), pp. 2616–2624 (2013)
Pastor, P., Righetti, L., Kalakrishnan, M., Schaal, S.: Online movement adaptation based on previous sensor experiences. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 365–371 (2011)
Peters, J., Mülling, K., Altün, Y.: Relative entropy policy search. In: AAAI Conference on Artificial Intelligence, pp. 1607–1612 (2010)
Pignat, E., Calinon, S.: Learning adaptive dressing assistance from human demonstration. Robot. Auton. Syst. 93, 61–75 (2017)
Stulp, F., Oztop, E., Pastor, P., Beetz, M., Schaal, S.: Compact models of motor primitive variations for predictable reaching and obstacle avoidance. In 2009 9th IEEE-RAS International Conference on Humanoid Robots, pp. 589–595 (2009)
Tausz, A., Vejdemo-Johansson, M., Adams, H.: Javaplex: a research software package for persistent (co)homology. In: International Conference on Mathematical Software, pp. 129–136 (2014)
Toussaint, M.: Lecture notes: Gaussian identities. http://ipvs.informatik.uni-stuttgart.de/mlr/marc/notes/gaussians.pdf
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Colomé, A., Torras, C. (2020). Generating and Adapting Probabilistic Movement Primitives. In: Reinforcement Learning of Bimanual Robot Skills. Springer Tracts in Advanced Robotics, vol 134. Springer, Cham. https://doi.org/10.1007/978-3-030-26326-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-26326-3_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26325-6
Online ISBN: 978-3-030-26326-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)