Abstract
In a recent study, it was demonstrated that Recurrent Neural Networks (RNNs) can be used to effectively control snake-like, many-joint robot arms in a particular way: The inverse kinematics for control are generated using back-propagation through time (BPTT) on recurrent forward models that learned to predict the end-effector pose of a robot arm, whereby each joint is associated with a certain computation time step of the RNN. This paper further investigates this approach in terms of constraint-aware control. Our contribution is twofold: First, we show that an RNN can be trained to also predict the poses of intermediate joints within such an arm, and that these can consequently be included in the control-optimization objective as well, giving full control over the entire arm. Second, we show that particular components of the arm’s target can be selectively switched on and off by means of “don’t care” signals. This enables us to handle constraints inherently and on-the-fly, without the need of any outer constraint mechanisms, such as additional penalty terms. The experiments demonstrating the effectiveness of our methodology are carried out on a simulated three dimensional 40-joint robot arm with 80 articulated degrees offreedom.
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References
Calinon, S., Guenter, F., Billard, A.: On learning, representing, and generalizing a task in a humanoid robot. IEEE Trans. Syst. Man Cybern. Part B Cybern. 37(2), 286–298 (2007)
Ehrenfeld, S., Butz, M.V.: The modular modality frame model: continuous body state estimation and plausibility-weighted information fusion. Biol. Cybern. 107, 61–82 (2013)
Friston, K.: The free-energy principle: a rough guide to the brain? Trends Cogn. Sci. 13(7), 293–301 (2009)
Friston, K.: The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11(2), 127–138 (2010)
Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., Pezzulo, G.: Active inference: a process theory. Neural Comput. 29(1), 1–49 (2016)
Graves, A., Fernández, S., Schmidhuber, J.: Multi-dimensional recurrent neural networks. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds.) ICANN 2007. LNCS, vol. 4668, pp. 549–558. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74690-4_56
Gumbsch, C., Kneissler, J., Butz, M.V.: Learning behavior-grounded event segmentations. In: Papafragou, A., Grodner, D., Mirman, D., Trueswell, J.C. (eds.) Proceedings of the 38th Annual Meeting of the Cognitive Science Society, pp. 1787–1792. Cognitive Science Society, Austin (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Kingma, D.P., Ba, J.L.: Adam: A method for stochastic optimization. In: 3rd International Conference for Learning Representations abs/1412.6980 (2015)
Neumann, M., Burgner-Kahrs, J.: Considerations for follow-the-leader motion of extensible tendon-driven continuum robots. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 917–923, May 2016
Otte, S., Krechel, D., Liwicki, M.: JANNLab neural network framework for Java. In: Poster Proceedings MLDM 2013, pp. 39–46. ibai-publishing, New York (2013)
Otte, S., Liwicki, M., Zell, A.: Dynamic cortex memory: enhancing recurrent neural networks for gradient-based sequence learning. In: Wermter, S., Weber, C., Duch, W., Honkela, T., Koprinkova-Hristova, P., Magg, S., Palm, G., Villa, A.E.P. (eds.) ICANN 2014. LNCS, vol. 8681, pp. 1–8. Springer, Cham (2014). doi:10.1007/978-3-319-11179-7_1
Otte, S., Liwicki, M., Zell, A.: An analysis of dynamic cortex memory networks. In: International Joint Conference on Neural Networks (IJCNN), pp. 3338–3345. Killarney, Ireland, July 2015
Otte, S., Zwiener, A., Hanten, R., Zell, A.: Inverse recurrent models – an application scenario for many-joint robot arm control. In: Villa, A.E.P., Masulli, P., Pons Rivero, A.J. (eds.) ICANN 2016. LNCS, vol. 9886, pp. 149–157. Springer, Cham (2016). doi:10.1007/978-3-319-44778-0_18
Schilling, M.: Universally manipulable body models - dual quaternion representations in layered and dynamic MMCs. Auton. Robots 30, 399–425 (2011)
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Otte, S., Zwiener, A., Butz, M.V. (2017). Inherently Constraint-Aware Control of Many-Joint Robot Arms with Inverse Recurrent Models. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10613. Springer, Cham. https://doi.org/10.1007/978-3-319-68600-4_31
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DOI: https://doi.org/10.1007/978-3-319-68600-4_31
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