KineFormer: Solving the Inverse Modeling Problem of Soft Robots Using Transformers
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-64495-5_3
Reference15 articles.
1. Zhang, Z., Bieze, T.M., Dequidt, J., Kruszewski, A., Duriez, C.: Visual servoing control of soft robots based on finite element model. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2895–2901 (2017)
2. Largilliere, F., Verona, V., Coevoet, E., Sanz-Lopez, M., Dequidt, J., Duriez, C.: Real-time control of soft-robots using asynchronous finite element modeling. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 2550–2555 (2015)
3. Holsten, F., Engell-Norregard, M.P., Darkner, S., Erleben, K.: Data driven inverse kinematics of soft robots using local models. In: International Conference on Robotics and Automation (ICRA), pp. 6251–6257 (2019)
4. Gillespie, M.T., Best, C.M., Townsend, E.C., Wingate, D., Killpack, M.D.: Learning nonlinear dynamic models of soft robots for model predictive control with neural networks. In: IEEE International Conference on Soft Robotics (RoboSoft), pp. 39–45 (2018)
5. Bern, J.M., Schnider, Y., Banzet, P., Kumar, N., Coros, S.: Soft robot control with a learned differentiable model. In: 3rd IEEE International Conference on Soft Robotics (RoboSoft), pp. 417–423 (2020)
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