Characterizing Manipulation Robustness through Energy Margin and Caging Analysis (2404.12115v2)
Abstract: To develop robust manipulation policies, quantifying robustness is essential. Evaluating robustness in general manipulation, nonetheless, poses significant challenges due to complex hybrid dynamics, combinatorial explosion of possible contact interactions, global geometry, etc. This paper introduces an approach for evaluating manipulation robustness through energy margins and caging-based analysis. Our method assesses manipulation robustness by measuring the energy margin to failure and extends traditional caging concepts for dynamic manipulation. This global analysis is facilitated by a kinodynamic planning framework that naturally integrates global geometry, contact changes, and robot compliance. We validate the effectiveness of our approach in simulation and real-world experiments of multiple dynamic manipulation scenarios, highlighting its potential to predict manipulation success and robustness.
- A. Bhatt, A. Sieler, S. Puhlmann, and O. Brock, “Surprisingly robust in-hand manipulation: An empirical study,” arXiv preprint arXiv:2201.11503, 2022.
- H. Tsukamoto, S.-J. Chung, and J.-J. E. Slotine, “Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview,” Annual Reviews in Control, vol. 52, pp. 135–169, 2021.
- R. Tedrake et al., “Lqr-trees: Feedback motion planning on sparse randomized trees.” in Robotics: Science and Systems, vol. 2009, 2009.
- A. D. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath, and P. Tabuada, “Control barrier functions: Theory and applications,” in European control conference. IEEE, 2019, pp. 3420–3431.
- M. A. Roa and R. Suárez, “Grasp quality measures: review and performance,” Autonomous robots, vol. 38, pp. 65–88, 2015.
- Y. Hou and M. T. Mason, “Criteria for maintaining desired contacts for quasi-static systems,” in Proc. Int. Conf. Intell. Robot. Syst. IEEE, 2019, pp. 6555–6561.
- S. Makita and W. Wan, “A survey of robotic caging and its applications,” Advanced Robotics, vol. 31, no. 19-20, pp. 1071–1085, 2017.
- Y. Dong and F. T. Pokorny, “Quasi-static soft fixture analysis of rigid and deformable objects,” Proc. Int. Conf. Robot. Automat., 2024.
- W. Kuperberg, “Problems on polytopes and convex sets,” in DIMACS Workshop on polytopes, 1990, pp. 584–589.
- E. Rimon and A. Blake, “Caging 2d bodies by 1-parameter two-fingered gripping systems,” in Proc. Int. Conf. Robot. Automat., vol. 2. IEEE, 1996, pp. 1458–1464.
- ——, “Caging planar bodies by one-parameter two-fingered gripping systems,” IEEE Int. J. Robot. Res., vol. 18, no. 3, pp. 299–318, 1999.
- A. Rodriguez, M. T. Mason, and S. Ferry, “From caging to grasping,” IEEE Int. J. Robot. Res., vol. 31, no. 7, pp. 886–900, 2012.
- J. Mahler, F. T. Pokorny, Z. McCarthy, A. F. van der Stappen, and K. Goldberg, “Energy-bounded caging: Formal definition and 2-d energy lower bound algorithm based on weighted alpha shapes,” IEEE Robot. Automat. Lett., vol. 1, no. 1, pp. 508–515, 2016.
- J. Mahler, F. T. Pokorny, S. Niyaz, and K. Goldberg, “Synthesis of energy-bounded planar caging grasps using persistent homology,” IEEE Trans. Automat. Sci. Eng., vol. 15, no. 3, pp. 908–918, 2018.
- R. R. Ma, W. G. Bircher, and A. M. Dollar, “Modeling and evaluation of robust whole-hand caging manipulation,” IEEE Trans. Robot., vol. 35, no. 3, pp. 549–563, 2019.
- N. S. Pollard, “Synthesizing grasps from generalized prototypes,” in Proc. Int. Conf. Robot. Automat., vol. 3. IEEE, 1996, pp. 2124–2130.
- A. T. Miller and P. K. Allen, “Examples of 3d grasp quality computations,” in Proc. Int. Conf. Robot. Automat., vol. 2. IEEE, 1999, pp. 1240–1246.
- C. Ferrari, J. F. Canny, et al., “Planning optimal grasps.” in Proc. Int. Conf. Robot. Automat., vol. 3, no. 4, 1992, p. 6.
- Y. Lin and Y. Sun, “Task-based grasp quality measures for grasp synthesis,” in Proc. Int. Conf. Intell. Robot. Syst. IEEE, 2015, pp. 485–490.
- J. Xu, M. Danielczuk, J. Ichnowski, J. Mahler, E. Steinbach, and K. Goldberg, “Minimal work: A grasp quality metric for deformable hollow objects,” in Proc. Int. Conf. Robot. Automat. IEEE, 2020, pp. 1546–1552.
- A. Saxena, L. L. Wong, and A. Y. Ng, “Learning grasp strategies with partial shape information.” in AAAI, vol. 3, no. 2, 2008, pp. 1491–1494.
- R. Krug, Y. Bekiroglu, D. Kragic, and M. A. Roa, “Evaluating the quality of non-prehensile balancing grasps,” in Proc. Int. Conf. Robot. Automat. IEEE, 2018, pp. 4215–4220.
- T. Makapunyo, T. Phoka, P. Pipattanasomporn, N. Niparnan, and A. Sudsang, “Measurement framework of partial cage quality based on probabilistic motion planning,” in Proc. Int. Conf. Robot. Automat. IEEE, 2013, pp. 1574–1579.
- K. Hauser and Y. Zhou, “Asymptotically optimal planning by feasible kinodynamic planning in a state–cost space,” IEEE Trans. Robot., vol. 32, no. 6, pp. 1431–1443, 2016.
- D. Hsu, J.-C. Latombe, and R. Motwani, “Path planning in expansive configuration spaces,” in Proc. Int. Conf. Robot. Automat., vol. 3. IEEE, 1997, pp. 2719–2726.
- S. M. LaValle, “Rapidly-exploring random trees: A new tool for path planning,” Research Report 9811, 1998.
- J. Liu, F. Feng, Y. C. Nakamura, and N. S. Pollard, “A taxonomy of everyday grasps in action,” in Proc. Int. Conf. Humanoid Robot. IEEE, 2014, pp. 573–580.
- F. Ruggiero, V. Lippiello, and B. Siciliano, “Nonprehensile dynamic manipulation: A survey,” IEEE Robot. Automat. Lett., vol. 3, no. 3, pp. 1711–1718, 2018.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.