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Simultaneous Trajectory Optimization and Contact Selection for Contact-rich Manipulation with High-Fidelity Geometry (2407.16976v1)

Published 24 Jul 2024 in cs.RO

Abstract: Contact-implicit trajectory optimization (CITO) is an effective method to plan complex trajectories for various contact-rich systems including manipulation and locomotion. CITO formulates a mathematical program with complementarity constraints (MPCC) that enforces that contact forces must be zero when points are not in contact. However, MPCC solve times increase steeply with the number of allowable points of contact, which limits CITO's applicability to problems in which only a few, simple geometries are allowed to make contact. This paper introduces simultaneous trajectory optimization and contact selection (STOCS), as an extension of CITO that overcomes this limitation. The innovation of STOCS is to identify salient contact points and times inside the iterative trajectory optimization process. This effectively reduces the number of variables and constraints in each MPCC invocation. The STOCS framework, instantiated with key contact identification subroutines, renders the optimization of manipulation trajectories computationally tractable even for high-fidelity geometries consisting of tens of thousands of vertices.

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References (28)
  1. C. Eppner and O. Brock, “Planning grasp strategies that exploit environmental constraints,” in 2015 IEEE international conference on robotics and automation (ICRA).   IEEE, 2015, pp. 4947–4952.
  2. M. Kelly, “An introduction to trajectory optimization: How to do your own direct collocation,” SIAM Review, vol. 59, no. 4, pp. 849–904, 2017.
  3. G. Schultz and K. Mombaur, “Modeling and optimal control of human-like running,” IEEE/ASME Transactions on mechatronics, vol. 15, no. 5, pp. 783–792, 2009.
  4. M. Posa, C. Cantu, and R. Tedrake, “A direct method for trajectory optimization of rigid bodies through contact,” The International Journal of Robotics Research, vol. 33, no. 1, pp. 69–81, 2014.
  5. I. Mordatch, Z. Popović, and E. Todorov, “Contact-invariant optimization for hand manipulation,” in Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 2012, pp. 137–144.
  6. I. Mordatch, E. Todorov, and Z. Popović, “Discovery of complex behaviors through contact-invariant optimization,” ACM Transactions on Graphics (TOG), vol. 31, no. 4, pp. 1–8, 2012.
  7. A. Patel, S. L. Shield, S. Kazi, A. M. Johnson, and L. T. Biegler, “Contact-implicit trajectory optimization using orthogonal collocation,” IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 2242–2249, 2019.
  8. A. Ö. Önol, R. Corcodel, P. Long, and T. Padır, “Tuning-free contact-implicit trajectory optimization,” in 2020 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2020, pp. 1183–1189.
  9. M. Zhang and K. Hauser, “Semi-infinite programming with complementarity constraints for pose optimization with pervasive contact,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 6329–6335.
  10. K. Harada, K. Hauser, T. Bretl, and J.-C. Latombe, “Natural motion generation for humanoid robots,” in 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2006, pp. 833–839.
  11. N. Chavan-Dafle, R. Holladay, and A. Rodriguez, “Planar in-hand manipulation via motion cones,” The International Journal of Robotics Research, vol. 39, no. 2-3, pp. 163–182, 2020.
  12. Y. Shirai, D. K. Jha, A. U. Raghunathan, and D. Romeres, “Robust pivoting: Exploiting frictional stability using bilevel optimization,” in 2022 International Conference on Robotics and Automation (ICRA).   IEEE, 2022, pp. 992–998.
  13. X. Cheng, E. Huang, Y. Hou, and M. T. Mason, “Contact mode guided sampling-based planning for quasistatic dexterous manipulation in 2d,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 6520–6526.
  14. ——, “Contact mode guided motion planning for quasidynamic dexterous manipulation in 3d,” in 2022 International Conference on Robotics and Automation (ICRA).   IEEE, 2022, pp. 2730–2736.
  15. E. Huang, X. Cheng, and M. T. Mason, “Efficient contact mode enumeration in 3d,” in International Workshop on the Algorithmic Foundations of Robotics.   Springer, 2021, pp. 485–501.
  16. M. Zhang and K. Hauser, “Semi-infinite programming with complementarity constraints for pose optimization with pervasive contact,” May 2021.
  17. M. Zhang, D. K. Jha, A. U. Raghunathan, and K. Hauser, “Simultaneous trajectory optimization and contact selection for multi-modal manipulation planning,” arXiv preprint arXiv:2306.06465, 2023.
  18. K. Hauser, “Semi-infinite programming for trajectory optimization with non-convex obstacles,” The International Journal of Robotics Research, vol. 40, no. 10-11, pp. 1106–1122, 2021.
  19. D. E. Stewart and J. C. Trinkle, “An implicit time-stepping scheme for rigid body dynamics with inelastic collisions and coulomb friction,” International Journal for Numerical Methods in Engineering, vol. 39, no. 15, pp. 2673–2691, 1996.
  20. M. López and G. Still, “Semi-infinite programming,” European Journal of Operational Research, vol. 180, no. 2, pp. 491–518, 2007.
  21. R. Reemtsen and S. Görner, “Numerical methods for semi-infinite programming: a survey,” in Semi-Infinite Programming.   Springer, 1998, pp. 195–275.
  22. P. E. Gill, W. Murray, and M. A. Saunders, “Snopt: An sqp algorithm for large-scale constrained optimization,” SIAM review, vol. 47, no. 1, pp. 99–131, 2005.
  23. R. Tedrake and the Drake Development Team, “Drake: Model-based design and verification for robotics,” 2019. [Online]. Available: https://drake.mit.edu
  24. B. Calli, A. Walsman, A. Singh, S. Srinivasa, P. Abbeel, and A. M. Dollar, “Benchmarking in manipulation research: The ycb object and model set and benchmarking protocols,” arXiv preprint arXiv:1502.03143, 2015.
  25. L. Downs, A. Francis, N. Koenig, B. Kinman, R. Hickman, K. Reymann, T. B. McHugh, and V. Vanhoucke, “Google scanned objects: A high-quality dataset of 3d scanned household items,” in 2022 International Conference on Robotics and Automation (ICRA).   IEEE, 2022, pp. 2553–2560.
  26. Open Robotics. [Online]. Available: https://app.gazebosim.org/OpenRobotics/fuel/models/Sofa
  27. Sketchfab. [Online]. Available: https://sketchfab.com/3d-models/burlap-pillow-1ef567278bba4db68ac5488d6b4bc851
  28. Klampt – Intelligent Motion Laboratory at UIUC. [Online]. Available: http://motion.cs.illinois.edu/klampt/
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Authors (4)
  1. Mengchao Zhang (45 papers)
  2. Devesh K. Jha (46 papers)
  3. Arvind U. Raghunathan (21 papers)
  4. Kris Hauser (35 papers)

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