Papers
Topics
Authors
Recent
Search
2000 character limit reached

Pseudo-rigid body networks: learning interpretable deformable object dynamics from partial observations

Published 16 Jul 2023 in cs.RO and cs.LG | (2307.07975v4)

Abstract: Accurately predicting deformable linear object (DLO) dynamics is challenging, especially when the task requires a model that is both human-interpretable and computationally efficient. In this work, we draw inspiration from the pseudo-rigid body method (PRB) and model a DLO as a serial chain of rigid bodies whose internal state is unrolled through time by a dynamics network. This dynamics network is trained jointly with a physics-informed encoder that maps observed motion variables to the DLO's hidden state. To encourage the state to acquire a physically meaningful representation, we leverage the forward kinematics of the PRB model as a decoder. We demonstrate in robot experiments that the proposed DLO dynamics model provides physically interpretable predictions from partial observations while being on par with black-box models regarding prediction accuracy. The project code is available at: http://tinyurl.com/prb-networks

Definition Search Book Streamline Icon: https://streamlinehq.com
References (39)
  1. J. Sanchez, J.-A. Corrales, B.-C. Bouzgarrou, and Y. Mezouar, “Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey,” The International Journal of Robotics Research, vol. 37, no. 7, pp. 688–716, 2018.
  2. P. Jiménez, “Survey on model-based manipulation planning of deformable objects,” Robotics and computer-integrated manufacturing, vol. 28, no. 2, pp. 154–163, 2012.
  3. W. Wang, D. Berenson, and D. Balkcom, “An online method for tight-tolerance insertion tasks for string and rope,” in 2015 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2015, pp. 2488–2495.
  4. E. Yoshida, K. Ayusawa, I. G. Ramirez-Alpizar, K. Harada, C. Duriez, and A. Kheddar, “Simulation-based optimal motion planning for deformable object,” in 2015 IEEE international workshop on advanced robotics and its social impacts (ARSO).   IEEE, 2015, pp. 1–6.
  5. M. Yan, Y. Zhu, N. Jin, and J. Bohg, “Self-supervised learning of state estimation for manipulating deformable linear objects,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 2372–2379, 2020.
  6. D. K. Pai, “Strands: Interactive simulation of thin solids using cosserat models,” in Computer graphics forum, vol. 21.   Wiley Online Library, 2002, pp. 347–352.
  7. M. Saha and P. Isto, “Manipulation planning for deformable linear objects,” IEEE Transactions on Robotics, vol. 23, no. 6, 2007.
  8. A. Verl, A. Valente, S. Melkote, C. Brecher, E. Ozturk, and L. T. Tunc, “Robots in machining,” CIRP Annals, vol. 68, no. 2, pp. 799–822, 2019.
  9. M. Lutter, C. Ritter, and J. Peters, “Deep lagrangian networks: Using physics as model prior for deep learning,” in International Conference on Learning Representations, 2019.
  10. M. Cranmer, S. Greydanus, S. Hoyer, P. Battaglia, D. Spergel, and S. Ho, “Lagrangian neural networks,” in ICLR 2020 Workshop on Integration of Deep Neural Models and Differential Equations, 2019.
  11. L. Rath, A. R. Geist, and S. Trimpe, “Using physics knowledge for learning rigid-body forward dynamics with gaussian process force priors,” in Conference on Robot Learning.   PMLR, 2022, pp. 101–111.
  12. D. Nguyen-Tuong and J. Peters, “Using model knowledge for learning inverse dynamics,” in 2010 IEEE International Conference on Robotics and Automation, 2010, pp. 2677–2682.
  13. M. Lutter and J. Peters, “Combining physics and deep learning to learn continuous-time dynamics models,” The International Journal of Robotics Research, vol. 42, no. 3, pp. 83–107, 2023.
  14. J. Spillmann and M. Teschner, “Corde: Cosserat rod elements for the dynamic simulation of one-dimensional elastic objects,” in Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation, 2007, pp. 63–72.
  15. S. Drücker and R. Seifried, “Application of stable inversion to flexible manipulators modeled by the absolute nodal coordinate formulation,” GAMM-Mitteilungen, vol. 46, no. 1, 2023.
  16. J. Kim and N. S. Pollard, “Fast simulation of skeleton-driven deformable body characters,” ACM Transactions on Graphics (TOG), vol. 30, no. 5, pp. 1–19, 2011.
  17. M. Bergou, M. Wardetzky, S. Robinson, B. Audoly, and E. Grinspun, “Discrete elastic rods,” in ACM SIGGRAPH 2008, 2008, pp. 1–12.
  18. F. Boyer, A. Gotelli, P. Tempel, V. Lebastard, F. Renda, and S. Briot, “Implicit time-integration simulation of robots with rigid bodies and cosserat rods based on a newton–euler recursive algorithm,” IEEE Transactions on Robotics, vol. 40, pp. 677–696, 2024.
  19. H. Lang, J. Linn, and M. Arnold, “Multi-body dynamics simulation of geometrically exact cosserat rods,” Multibody System Dynamics, vol. 25, no. 3, pp. 285–312, 2011.
  20. R. J. Webster III and B. A. Jones, “Design and kinematic modeling of constant curvature continuum robots: A review,” The International Journal of Robotics Research, vol. 29, no. 13, pp. 1661–1683, 2010.
  21. F. Stella, N. Obayashi, C. D. Santina, and J. Hughes, “An experimental validation of the polynomial curvature model: Identification and optimal control of a soft underwater tentacle,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 11 410–11 417, 2022.
  22. F. Stella, Q. Guan, C. Della Santina, and J. Hughes, “Piecewise affine curvature model: a reduced-order model for soft robot-environment interaction beyond pcc,” in 2023 IEEE International Conference on Soft Robotics (RoboSoft), 2023, pp. 1–7.
  23. K. Tanaka, Y. Minami, Y. Tokudome, K. Inoue, Y. Kuniyoshi, and K. Nakajima, “Continuum-body-pose estimation from partial sensor information using recurrent neural networks,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 11 244–11 251, 2022.
  24. A. Tariverdi, V. K. Venkiteswaran, M. Richter, O. J. Elle, J. Tørresen, K. Mathiassen, S. Misra, and Ø. G. Martinsen, “A recurrent neural-network-based real-time dynamic model for soft continuum manipulators,” Frontiers in Robotics and AI, vol. 8, p. 631303, 2021.
  25. Y. Yang, J. A. Stork, and T. Stoyanov, “Learning to propagate interaction effects for modeling deformable linear objects dynamics,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 1950–1957.
  26. J. A. Preiss, D. Millard, T. Yao, and G. S. Sukhatme, “Tracking fast trajectories with a deformable object using a learned model,” in 2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 1351–1357.
  27. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997.
  28. F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE transactions on neural networks, vol. 20, no. 1, pp. 61–80, 2008.
  29. D. Q. Huynh, “Metrics for 3d rotations: Comparison and analysis,” Journal of Mathematical Imaging and Vision, vol. 35, pp. 155–164, 2009.
  30. R. Brégier, “Deep regression on manifolds: a 3d rotation case study,” in 2021 International Conference on 3D Vision (3DV).   IEEE, 2021, pp. 166–174.
  31. J. Kim, “Lie group formulation of articulated rigid body dynamics,” Technical report, Carnegie Mellon University, Tech. Rep., 2012.
  32. J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang, “JAX: composable transformations of Python+NumPy programs,” 2018. [Online]. Available: http://github.com/google/jax
  33. M. I. Jordan, “Constrained supervised learning,” Journal of Mathematical Psychology, vol. 36, no. 3, pp. 396–425, 1992.
  34. A. Sanchez-Gonzalez, J. Godwin, T. Pfaff, R. Ying, J. Leskovec, and P. Battaglia, “Learning to simulate complex physics with graph networks,” in International conference on machine learning.   PMLR, 2020, pp. 8459–8468.
  35. B. Siciliano, L. Sciavicco, L. Villani, and G. Oriolo, “Modelling, planning and control,” Advanced Textbooks in Control and Signal Processing. Springer,, 2009.
  36. K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078, 2014.
  37. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
  38. P. Kidger and C. Garcia, “Equinox: neural networks in JAX via callable PyTrees and filtered transformations,” Differentiable Programming workshop at Neural Information Processing Systems 2021, 2021.
  39. P. Dufter, M. Schmitt, and H. Schütze, “Position Information in Transformers: An Overview,” Computational Linguistics, vol. 48, no. 3, pp. 733–763, 09 2022. [Online]. Available: https://doi.org/10.1162/coli_a_00445
Citations (2)
View on Semantic Scholar

Summary

No one has generated a summary of this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Sign Up to Summarize

Paper to Video (Beta)

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up

Tweets

Sign up for free to view the 1 tweet with 0 likes about this paper.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up for Free