Abstract
This chapter raises open questions about kinematics and dynamics for humanoid robots and sketches some possible future trends and directions. This requires to think about possible use cases in order to make good predictions about the future of kinematic and dynamic models. While initially bipedal walking with its conceptual challenges was of large concern, more recently applications such as service robotics, disaster mitigation, elderly care, and medical robotics become more important. The chapter focuses on three respective future directions and argues that, first, novel concepts in mechatronics have to embrace modularity and failure tolerance and to focus on durability and endurance, concepts that may lead to a larger parameter variance in the mechanisms. Second, it is expected that real-time simulation will play a larger role and may even be included in the control loops while coupling elastic, hydraulic, or pneumatic elements with the multibody dynamics. Third, the chapter makes the prediction that the hybrid combination of data-driven and classical modeling will be key toward becoming more flexible and possibly including more soft elements also in humanoid robots. The chapter concludes with the prediction that the future of kinematic and dynamic modeling is to combine advances in classical rigid-body computations with soft mechatronics, real-time simulation, and a mix of fast computing of classical algorithms and learning methods.
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References
P. Abbeel, A.Y. Ng, Apprenticeship learning via inverse reinforcement learning, in Proceedings of the Twenty-First International Conference on Machine Learning (ACM, 2004), p. 1
P.W. Battaglia, J.B. Hamrick, J.B. Tenenbaum, Simulation as an engine of physical scene understanding. Proc. Natl. Acad. Sci. 110(45), 18327–18332 (2013)
K.M. Ben-Gharbia, A.A. Maciejewski, R.G. Roberts, Kinematic design of manipulators with seven revolute joints optimized for fault tolerance. IEEE Trans. Syst. Man Cybern. 46(10), 1364–1373 (2016)
H. Bremer, Elastic Multibody Dynamics (Springer, Dordrecht, 2008)
T Buschmann, Dynamics and control of redundant robots, Technical University of Munich, 2015
K. Caluwaerts, J.J. Steil, Independent joint learning in practice: local error estimates to improve inverse dynamics control, in Humanoids (IEEE, Danvers, 2015), pp. 643–650
K.M. Chai, C. Williams, S. Klanke, S. Vijayakumar, Multi-task gaussian process learning of robot inverse dynamics, in Advances in Neural Information Processing Systems, 2009, pp. 265–272
L. Colasanto, N.G. Tsagarakis, A.J. Ijspeert, A general whole-body compliance framework for humanoid robots, in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2015), pp. 3962–3968
B. Damas, J. Santos-Victor, An online algorithm for simultaneously learning forward and inverse kinematics, in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2012), pp. 1499–1506
A. Dearden, Y. Demiris, Learning forward models for robots. IJCAI 5, 1440 (2005)
R. Deimel, O. Brock, Soft hands for reliable grasping strategies, in Soft Robotics, ed. by A. Verl, A. Albu-Schäffer, O. Brock, A. Raatz (Springer, Berlin/Heidelberg), pp. 211–221
M. Florek-Jasińska, T. Wimböck, C. Ott, Humanoid compliant whole arm dexterous manipulation: control design and experiments, in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2014), pp. 1616–1621
M. Gienger, H. Janssen, C. Goerick, Task-oriented whole body motion for humanoid robots, in 2005 5th IEEE-RAS International Conference on Humanoid Robots (IEEE, 2005), pp. 238–244
J.A. Grimes, J.W. Hurst, The design of atrias 1.0 a unique monopod, hopping robot, in Proceedings of the 2012 International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, 2012, pp. 548–554
I. Ha, Y. Tamura, H. Asama, J. Han, D.W. Hong, Development of open humanoid platform darwin-op, in 2011 Proceedings of SICE Annual Conference (SICE) (IEEE, 2011), pp. 2178–2181
M. Hersch, E. Sauser, A. Billard, Online learning of the body schema. Int. J. Humanoid Rob. 5(02), 161–181 (2008)
S. Ivaldi, J. Peters, V. Padois, F. Nori, Tools for simulating humanoid robot dynamics: a survey based on user feedback, in 2014 IEEE-RAS International Conference on Humanoid Robots (IEEE, 2014), pp. 842–849
A. Jain, M.D. Killpack, A. Edsinger, C.C. Kemp, Reaching in clutter with whole-arm tactile sensing. Int. J. Robot. Res. 32, 458–482 (2013)
M.I. Jordan, D.E. Rumelhart, Forward models: supervised learning with a distal teacher. Cogn. Sci. 16(3), 307–354 (19920
S. Kim, C. Laschi, B. Trimmer, Soft robotics: a bioinspired evolution in robotics. Trends Biotechnol. 31(5), 287–294 (2013)
S. Kim, A. Shukla, A. Billard, Catching objects in flight. IEEE Trans. Robot. 30(5), 1049–1065 (2014)
N. Koenig, A. Howard, Design and use paradigms for gazebo, an open-source multi-robot simulator, in Proceedings. 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 3 (IEEE), pp. 2149–2154
M. Lapeyre, P. Rouanet, J. Grizou, S. Nguyen, F. Depraetre, A. Le Falher, P.-Y. Oudeyer, Poppy project: open-source fabrication of 3D printed humanoid robot for science, education and art, in Digital Intelligence 2014, Nantes, 2014, p. 6
K. Loffler, M. Gienger, F. Pfeiffer, Control of a biped jogging robot, in 2000 Proceedings of 6th International Workshop on Advanced Motion Control (IEEE, 2000), pp. 601–605
M. Lopes, B. Damas, A learning framework for generic sensory-motor maps, in IROS, 2007, pp. 1533–1538
F. Meier, D. Kappler, N. Ratliff, S. Schaal, Towards robust online inverse dynamics learning, in Proceedings of IROS (IEEE, 2016), pp. 4034–4039
K. Mombaur, A. Truong, J.-P. Laumond, From human to humanoid locomotion – an inverse optimal control approach. Auton. Robot. 28(3), 369–383 (2010)
D. Nguyen-Tuong, J. Peters, Using model knowledge for learning inverse dynamics, in ICRA, 2010, pp. 2677–2682
D. Nguyen-Tuong, J. Peters, Model learning for robot control: a survey. Cogn. Process. 12(4), 319–340 (2011)
G. Pratt, J. Manzo, The darpa robotics challenge [competitions]. IEEE Robot. Autom. Mag. 20(2), 10–12 (2013)
G.A. Pratt, M.M. Williamson, Series elastic actuators, in Proceedings. 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems’95. Human Robot Interaction and Cooperative Robots, vol. 1 (IEEE, 1995), pp. 399–406
R.F. Reinhart, J.J. Steil, Neural learning and dynamical selection of redundant solutions for inverse kinematic control, in 2011 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids) (IEEE, 2011), pp. 564–569
M. Rolf, J.J. Steil, M. Gienger, Goal babbling permits direct learning of inverse kinematics. IEEE Trans. Auton. Ment. Dev. 2(3), 216–229 (2010)
M. Rolf, J.J. Steil, Efficient exploratory learning of inverse kinematics on a bionic elephant trunk. IEEE Trans. Neural Netw. Learn. Syst. 25(6), 1147–1160 (2014)
M. Rolf, J.J. Steil, M. Gienger, Learning flexible full body kinematics for humanoid tool use, in International Symposium on Learning and Adaptive Behavior in Robotic Systems, 2010
M. Rolf, J.J. Steil, M. Gienger, Mastering growth while bootstrapping sensorimotor coordination, in International Conference on Epigenetic Robotics, 2010
G. Schillaci, V.V. Hafner, Random movement strategies in self-exploration for a humanoid robot, in Proceedings of the 6th International Conference on Human-Robot Interaction (ACM, 2011), pp. 245–246
J. Scholz, M. Levihn, C. Isbell, D. Wingate, A physics-based model prior for object-oriented MDPs, in Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014, pp. 1089–1097
Z. Shareef, F. Reinhart, J. Steil, Generalizing the inverse dynamical model of KUKA LWR IV+ for load variations using regression in the model space, in Proceedings of IROS (IEEE, 2016), pp. 606–611
A.D. Souza, S. Vijayakumar, S. Schaal, Learning inverse kinematics, in Proceedings. 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1 (IEEE, 2001), pp. 298–303
N.G. Tsagarakis, G. Metta, G. Sandini, D. Vernon, R. Beira, F. Becchi, L. Righetti, J. Santos-Victor, A.J. Ijspeert, M.C. Carrozza et al., iCub: the design and realization of an open humanoid platform for cognitive and neuroscience research. Adv. Robot. 21(10), 1151–1175 (2007)
T.T. Um, M.S. Park, J.M. Park, Independent joint learning: a novel task-to-task transfer learning scheme for robot models, in ICRA, 2014, pp. 5679–5684
B. Vanderborght, A. Albu-Schäffer, A. Bicchi, E. Burdet, D.G. Caldwell, R. Carloni, M. Catalano, O. Eiberger, W. Friedl, G. Ganesh et al., Variable impedance actuators: a review. Robot. Auton. Syst. 61(12), 1601–1614 (2013)
S. Vijayakumar, A. D’souza, S. Schaal, Incremental online learning in high dimensions. Neural Comput. 17(12), 2602–2634 (2005)
S. Wittmeier, C. Alessandro, N. Bascarevic, K. Dalamagkidis, D. Devereux, A. Diamond, M. Jäntsch, K. Jovanovic, R. Knight, H.G. Marques et al., Toward anthropomimetic robotics: development, simulation, and control of a musculoskeletal torso. Artif. Life 19(1), 171–193 (2013)
S. Wrede, C. Emmerich, R. Grünberg, A. Nordmann, A. Swadzba, J.J. Steil, A user study on kinesthetic teaching and learning for efficient reconfiguration of redundant robots. J. Human-Rob. Interaction 2(1), 56–81 (2013)
E. Yoshida, V. Hugel, P. Blazevic, K. Yokoi, K. Harada, in Dexterous Humanoid Whole-Body Manipulation by Pivoting, Humanoid Robots, Human-like Machines, ed. by M. Hackel (InTech, 2007), https://doi.org/10.5772/4818
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Gienger, M., Steil, J.J. (2019). Humanoid Kinematics and Dynamics: Open Questions and Future Directions. In: Goswami, A., Vadakkepat, P. (eds) Humanoid Robotics: A Reference. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6046-2_8
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DOI: https://doi.org/10.1007/978-94-007-6046-2_8
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