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Online Learning Feedback Control Considering Hysteresis for Musculoskeletal Structures (2405.11808v1)
Published 20 May 2024 in cs.RO
Abstract: While the musculoskeletal humanoid has various biomimetic benefits, its complex modeling is difficult, and many learning control methods have been developed. However, for the actual robot, the hysteresis of its joint angle tracking is still an obstacle, and realizing target posture quickly and accurately has been difficult. Therefore, we develop a feedback control method considering the hysteresis. To solve the problem in feedback controls caused by the closed-link structure of the musculoskeletal body, we update a neural network representing the relationship between the error of joint angles and the change in target muscle lengths online, and realize target joint angles accurately in a few trials. We compare the performance of several configurations with various network structures and loss definitions, and verify the effectiveness of this study on an actual musculoskeletal humanoid, Musashi.
- S. Wittmeier, C. Alessandro, N. Bascarevic, K. Dalamagkidis, D. Devereux, A. Diamond, M. Jäntsch, K. Jovanovic, R. Knight, H. G. Marques, P. Milosavljevic, B. Mitra, B. Svetozarevic, V. Potkonjak, R. Pfeifer, A. Knoll, and O. Holland, “Toward Anthropomimetic Robotics: Development, Simulation, and Control of a Musculoskeletal Torso,” Artificial Life, vol. 19, no. 1, pp. 171–193, 2013.
- M. Jäntsch, S. Wittmeier, K. Dalamagkidis, A. Panos, F. Volkart, and A. Knoll, “Anthrob - A Printed Anthropomimetic Robot,” in Proceedings of the 2013 IEEE-RAS International Conference on Humanoid Robots, 2013, pp. 342–347.
- Y. Asano, T. Kozuki, S. Ookubo, M. Kawamura, S. Nakashima, T. Katayama, Y. Iori, H. Toshinori, K. Kawaharazuka, S. Makino, Y. Kakiuchi, K. Okada, and M. Inaba, “Human Mimetic Musculoskeletal Humanoid Kengoro toward Real World Physically Interactive Actions,” in Proceedings of the 2016 IEEE-RAS International Conference on Humanoid Robots, 2016, pp. 876–883.
- K. Kawaharazuka, A. Miki, Y. Toshimitsu, K. Okada, and M. Inaba, “Adaptive Body Schema Learning System Considering Additional Muscles for Musculoskeletal Humanoids,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 3459–3466, 2022.
- K. Kawaharazuka, M. Nishiura, Y. Toshimitsu, Y. Omura, Y. Koga, Y. Asano, K. Okada, K. Kawasaki, and M. Inaba, “Robust Continuous Motion Strategy Against Muscle Rupture using Online Learning of Redundant Intersensory Networks for Musculoskeletal Humanoids,” Robotics and Autonomous Systems, vol. 152, pp. 1–14, 2022.
- K. Kawaharazuka, K. Tsuzuki, S. Makino, M. Onitsuka, Y. Asano, K. Okada, K. Kawasaki, and M. Inaba, “Long-time Self-body Image Acquisition and its Application to the Control of Musculoskeletal Structures,” IEEE Robotics and Automation Letters, vol. 4, no. 3, pp. 2965–2972, 2019.
- I. Mizuuchi, Y. Nakanishi, T. Yoshikai, M. Inaba, H. Inoue, and O. Khatib, “Body Information Acquisition System of Redundant Musculo-Skeletal Humanoid,” in Experimental Robotics IX, 2006, pp. 249–258.
- S. Ookubo, Y. Asano, T. Kozuki, T. Shirai, K. Okada, and M. Inaba, “Learning Nonlinear Muscle-Joint State Mapping Toward Geometric Model-Free Tendon Driven Musculoskeletal Robots,” in Proceedings of the 2015 IEEE-RAS International Conference on Humanoid Robots, 2015, pp. 765–770.
- K. Kawaharazuka, S. Makino, M. Kawamura, Y. Asano, K. Okada, and M. Inaba, “Online Learning of Joint-Muscle Mapping using Vision in Tendon-driven Musculoskeletal Humanoids,” IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 772–779, 2018.
- K. Kawaharazuka, S. Makino, M. Kawamura, A. Fujii, Y. Asano, K. Okada, and M. Inaba, “Online Self-body Image Acquisition Considering Changes in Muscle Routes Caused by Softness of Body Tissue for Tendon-driven Musculoskeletal Humanoids,” in Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018, pp. 1711–1717.
- Y. Motegi, T. Shirai, T. Izawa, T. Kurotobi, J. Urata, Y. Nakanishi, K. Okada, and M. Inaba, “Motion control based on modification of the Jacobian map between the muscle space and work space with musculoskeletal humanoid,” in Proceedings of the 2012 IEEE-RAS International Conference on Humanoid Robots, 2012, pp. 835–840.
- V. D. Sapio, K. Holzbaur, and O. Khatib, “The control of kinematically constrained shoulder complexes: physiological and humanoid examples,” in Proceedings of the 2006 IEEE International Conference on Robotics and Automation, 2006, pp. 2952–2959.
- M. Jäntsch, S. Wittmeier, K. Dalamagkidis, G. Herrmann, and A. Knoll, “Adaptive neural network Dynamic Surface Control: An evaluation on the musculoskeletal robot Anthrob,” in Proceedings of the 2015 IEEE International Conference on Robotics and Automation, 2015, pp. 4347–4352.
- A. Diamond and O. E. Holland, “Reaching control of a full-torso, modelled musculoskeletal robot using muscle synergies emergent under reinforcement learning,” Bioinspiration & Biomimetics, vol. 9, no. 1, pp. 1–16, 2014.
- K. Kawaharazuka, S. Makino, K. Tsuzuki, M. Onitsuka, Y. Nagamatsu, K. Shinjo, T. Makabe, Y. Asano, K. Okada, K. Kawasaki, and M. Inaba, “Component Modularized Design of Musculoskeletal Humanoid Platform Musashi to Investigate Learning Control Systems,” in Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2019, pp. 7294–7301.
- J. Urata, Y. Nakanishi, A. Miyadera, I. Mizuuchi, T. Yoshikai, and M. Inaba, “A Three-Dimensional Angle Sensor for a Spherical Joint Using a Micro Camera,” in Proceedings of the 2006 IEEE International Conference on Robotics and Automation, 2006, pp. 4428–4430.
- D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” nature, vol. 323, no. 6088, pp. 533–536, 1986.
- D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” in Proceedings of the 3rd International Conference on Learning Representations, 2015, pp. 1–15.
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