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Functional Eigen-Grasping Using Approach Heatmaps (2401.11681v2)

Published 22 Jan 2024 in cs.RO

Abstract: This work presents a framework for a robot with a multi-fingered hand to freely utilize daily tools, including functional parts like buttons and triggers. An approach heatmap is generated by selecting a functional finger, indicating optimal palm positions on the object's surface that enable the functional finger to contact the tool's functional part. Once the palm position is identified through the heatmap, achieving the functional grasp becomes a straightforward process where the fingers stably grasp the object with low-dimensional inputs using the eigengrasp. As our approach does not need human demonstrations, it can easily adapt to various sizes and designs, extending its applicability to different objects. In our approach, we use directional manipulability to obtain the approach heatmap. In addition, we add two kinds of energy functions, i.e., palm energy and functional energy functions, to realize the eigengrasp. Using this method, each robotic gripper can autonomously identify its optimal workspace for functional grasping, extending its applicability to non-anthropomorphic robotic hands. We show that several daily tools like spray, drill, and remotes can be efficiently used by not only an anthropomorphic Shadow hand but also a non-anthropomorphic Barrett hand.

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References (27)
  1. A. Handa, K. Van Wyk, W. Yang, J. Liang, Y.-W. Chao, Q. Wan, S. Birchfield, N. Ratliff, and D. Fox, “Dexpilot: Vision-based teleoperation of dexterous robotic hand-arm system,” in 2020 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2020, pp. 9164–9170.
  2. Y. Qin, Y.-H. Wu, S. Liu, H. Jiang, R. Yang, Y. Fu, and X. Wang, “Dexmv: Imitation learning for dexterous manipulation from human videos,” in European Conference on Computer Vision.   Springer, 2022, pp. 570–587.
  3. S. Brahmbhatt, A. Handa, J. Hays, and D. Fox, “Contactgrasp: Functional multi-finger grasp synthesis from contact,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2019, pp. 2386–2393.
  4. A. Lakshmipathy, D. Bauer, C. Bauer, and N. S. Pollard, “Contact transfer: A direct, user-driven method for human to robot transfer of grasps and manipulations,” in 2022 International Conference on Robotics and Automation (ICRA).   IEEE, 2022, pp. 6195–6201.
  5. Y. Du, P. Weinzaepfel, V. Lepetit, and R. Brégier, “Multi-finger grasping like humans,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2022, pp. 1564–1570.
  6. M. T. Ciocarlie and P. K. Allen, “Hand posture subspaces for dexterous robotic grasping,” The International Journal of Robotics Research, vol. 28, no. 7, pp. 851–867, 2009.
  7. A. Kochan, “Shadow delivers first hand,” Industrial robot: an international journal, vol. 32, no. 1, pp. 15–16, 2005.
  8. W. Townsend, “The barretthand grasper–programmably flexible part handling and assembly,” Industrial Robot: an international journal, vol. 27, no. 3, pp. 181–188, 2000.
  9. D. Prattichizzo, M. Malvezzi, M. Gabiccini, and A. Bicchi, “On the manipulability ellipsoids of underactuated robotic hands with compliance,” Robotics and Autonomous Systems, vol. 60, no. 3, pp. 337–346, 2012.
  10. C. Rosales, R. Suárez, M. Gabiccini, and A. Bicchi, “On the synthesis of feasible and prehensile robotic grasps,” in 2012 IEEE international conference on robotics and automation.   IEEE, 2012, pp. 550–556.
  11. A. Bicchi and V. Kumar, “Robotic grasping and contact: A review,” in Proceedings 2000 ICRA. Millennium conference. IEEE international conference on robotics and automation. Symposia proceedings (Cat. No. 00CH37065), vol. 1.   IEEE, 2000, pp. 348–353.
  12. V.-D. Nguyen, “Constructing force-closure grasps,” The International Journal of Robotics Research, vol. 7, no. 3, pp. 3–16, 1988.
  13. C. Ferrari, J. F. Canny et al., “Planning optimal grasps.” in ICRA, vol. 3, no. 4, 1992, p. 6.
  14. H. B. Amor, O. Kroemer, U. Hillenbrand, G. Neumann, and J. Peters, “Generalization of human grasping for multi-fingered robot hands,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2012, pp. 2043–2050.
  15. J. Varley, J. Weisz, J. Weiss, and P. Allen, “Generating multi-fingered robotic grasps via deep learning,” in 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS).   IEEE, 2015, pp. 4415–4420.
  16. A. Rajeswaran, V. Kumar, A. Gupta, G. Vezzani, J. Schulman, E. Todorov, and S. Levine, “Learning complex dexterous manipulation with deep reinforcement learning and demonstrations,” arXiv preprint arXiv:1709.10087, 2017.
  17. S. Brahmbhatt, C. Ham, C. C. Kemp, and J. Hays, “Contactdb: Analyzing and predicting grasp contact via thermal imaging,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 8709–8719.
  18. P. Mandikal and K. Grauman, “Learning dexterous grasping with object-centric visual affordances,” in 2021 IEEE international conference on robotics and automation (ICRA).   IEEE, 2021, pp. 6169–6176.
  19. T. Zhu, R. Wu, X. Lin, and Y. Sun, “Toward human-like grasp: Dexterous grasping via semantic representation of object-hand,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 15 741–15 751.
  20. H. Dang and P. K. Allen, “Semantic grasping: planning task-specific stable robotic grasps,” Autonomous Robots, vol. 37, pp. 301–316, 2014.
  21. N. Vahrenkamp, T. Asfour, and R. Dillmann, “Efficient inverse kinematics computation based on reachability analysis,” International Journal of Humanoid Robotics, vol. 9, no. 04, p. 1250035, 2012.
  22. I. Akinola, J. Varley, B. Chen, and P. K. Allen, “Workspace aware online grasp planning,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2018, pp. 2917–2924.
  23. T. Yoshikawa, “Manipulability of robotic mechanisms,” The international journal of Robotics Research, vol. 4, no. 2, pp. 3–9, 1985.
  24. T. F. Chan and R. V. Dubey, “A weighted least-norm solution based scheme for avoiding joint limits for redundant joint manipulators,” IEEE transactions on Robotics and Automation, vol. 11, no. 2, pp. 286–292, 1995.
  25. L. McInnes, J. Healy, and S. Astels, “hdbscan: Hierarchical density based clustering,” The Journal of Open Source Software, vol. 2, no. 11, p. 205, 2017.
  26. S. Bambach, S. Lee, D. J. Crandall, and C. Yu, “Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions,” in Proceedings of the IEEE international conference on computer vision, 2015, pp. 1949–1957.
  27. Y. Hasson, G. Varol, D. Tzionas, I. Kalevatykh, M. J. Black, I. Laptev, and C. Schmid, “Learning joint reconstruction of hands and manipulated objects,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 11 807–11 816.

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