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In-Hand Following of Deformable Linear Objects Using Dexterous Fingers with Tactile Sensing

Published 19 Mar 2024 in cs.RO | (2403.12676v2)

Abstract: Most research on deformable linear object (DLO) manipulation assumes rigid grasping. However, beyond rigid grasping and re-grasping, in-hand following is also an essential skill that humans use to dexterously manipulate DLOs, which requires continuously changing the grasp point by in-hand sliding while holding the DLO to prevent it from falling. Achieving such a skill is very challenging for robots without using specially designed but not versatile end-effectors. Previous works have attempted using generic parallel grippers, but their robustness is unsatisfactory owing to the conflict between following and holding, which is hard to balance with a one-degree-of-freedom gripper. In this work, inspired by how humans use fingers to follow DLOs, we explore the usage of a generic dexterous hand with tactile sensing to imitate human skills and achieve robust in-hand DLO following. To enable the hardware system to function in the real world, we develop a framework that includes Cartesian-space arm-hand control, tactile-based in-hand 3-D DLO pose estimation, and task-specific motion design. Experimental results demonstrate the significant superiority of our method over using parallel grippers, as well as its great robustness, generalizability, and efficiency.

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References (31)
  1. J. Zhu, A. Cherubini, C. Dune, D. Navarro-Alarcon, F. Alambeigi, D. Berenson, F. Ficuciello, K. Harada, J. Kober, X. Li et al., “Challenges and outlook in robotic manipulation of deformable objects,” IEEE Robot. Autom. Mag., 2021.
  2. M. Yu, K. Lv, H. Zhong, S. Song, and X. Li, “Global model learning for large deformation control of elastic deformable linear objects: An efficient and adaptive approach,” IEEE Trans. Robot., vol. 39, no. 1, pp. 417–436, 2023.
  3. C. Wang, Y. Zhang, X. Zhang, Z. Wu, X. Zhu, S. Jin, T. Tang, and M. Tomizuka, “Offline-online learning of deformation model for cable manipulation with graph neural networks,” IEEE Robot. Autom. Lett., no. 2, pp. 5544–5551, 2022.
  4. M. Yu, H. Zhong, and X. Li, “Shape control of deformable linear objects with offline and online learning of local linear deformation models,” in IEEE Int. Conf. Robot. Autom., 2022, pp. 1337–1343.
  5. M. Yu, K. Lv, C. Wang, M. Tomizuka, and X. Li, “A coarse-to-fine framework for dual-arm manipulation of deformable linear objects with whole-body obstacle avoidance,” in IEEE Int. Conf. Robot. Autom., 2023, pp. 10 153–10 159.
  6. X. Huang, D. Chen, Y. Guo, X. Jiang, and Y. Liu, “Untangling multiple deformable linear objects in unknown quantities with complex backgrounds,” IEEE Trans. Autom. Sci. Eng., 2023.
  7. R. Lee, M. Hamaya, T. Murooka, Y. Ijiri, and P. Corke, “Sample-efficient learning of deformable linear object manipulation in the real world through self-supervision,” IEEE Robot. Autom. Lett., vol. 7, no. 1, pp. 573–580, 2022.
  8. S. Jin, W. Lian, C. Wang, M. Tomizuka, and S. Schaal, “Robotic cable routing with spatial representation,” IEEE Robot. Autom. Lett., vol. 7, no. 2, pp. 5687–5694, 2022.
  9. Y. She, S. Wang, S. Dong, N. Sunil, A. Rodriguez, and E. Adelson, “Cable manipulation with a tactile-reactive gripper,” Int. J. Robot. Res., vol. 40, no. 12-14, pp. 1385–1401, 2021.
  10. X. Jiang, Y. Nagaoka, K. Ishii, S. Abiko, T. Tsujita, and M. Uchiyama, “Robotized recognition of a wire harness utilizing tracing operation,” Robot. Comput. Integr. Manuf., vol. 34, pp. 52–61, 2015.
  11. J. Chapman, G. Gorjup, A. Dwivedi, S. Matsunaga, T. Mariyama, B. MacDonald, and M. Liarokapis, “A locally-adaptive, parallel-jaw gripper with clamping and rolling capable, soft fingertips for fine manipulation of flexible flat cables,” in IEEE Int. Conf. Robot. Autom., 2021, pp. 6941–6947.
  12. A. Wilson, H. Jiang, W. Lian, and W. Yuan, “Cable routing and assembly using tactile-driven motion primitives,” in IEEE Int. Conf. Robot. Autom., 2023, pp. 10 408–10 414.
  13. K. Shaw, A. Agarwal, and D. Pathak, “LEAP Hand: Low-cost, efficient, and anthropomorphic hand for robot learning,” Robotics: Science and Systems (RSS), 2023.
  14. A. Monguzzi, M. Pelosi, A. M. Zanchettin, and P. Rocco, “Tactile based robotic skills for cable routing operations,” in IEEE Int. Conf. Robot. Autom., 2023, pp. 3793–3799.
  15. L. Pecyna, S. Dong, and S. Luo, “Visual-tactile multimodality for following deformable linear objects using reinforcement learning,” in IEEE/RSJ Int. Conf. Intell. Robots Syst., 2022, pp. 3987–3994.
  16. R. B. Hellman, C. Tekin, M. van der Schaar, and V. J. Santos, “Functional contour-following via haptic perception and reinforcement learning,” IEEE Trans. Haptics, vol. 11, no. 1, pp. 61–72, 2017.
  17. K. Lv, M. Yu, Y. Pu, X. Jiang, G. Huang, and X. Li, “Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds,” in IEEE Int. Conf. Robot. Autom., 2023, pp. 7119–7125.
  18. S. Zhaole, H. Zhou, L. Nanbo, L. Chen, J. Zhu, and R. B. Fisher, “A robust deformable linear object perception pipeline in 3d: From segmentation to reconstruction,” IEEE Robot. Autom. Lett., vol. 9, no. 1, pp. 843–850, 2023.
  19. W. Yuan, S. Dong, and E. H. Adelson, “Gelsight: High-resolution robot tactile sensors for estimating geometry and force,” Sensors, vol. 17, no. 12, p. 2762, 2017.
  20. S. Pirozzi and C. Natale, “Tactile-based manipulation of wires for switchgear assembly,” IEEE/ASME Trans. on Mechatron., vol. 23, no. 6, pp. 2650–2661, 2018.
  21. Z. Yu, W. Xu, S. Yao, J. Ren, T. Tang, Y. Li, G. Gu, and C. Lu, “Precise robotic needle-threading with tactile perception and reinforcement learning,” in Conf. Robot Learn., 2023, pp. 3266–3276.
  22. F. Ficuciello, A. Migliozzi, E. Coevoet, A. Petit, and C. Duriez, “FEM-based deformation control for dexterous manipulation of 3d soft objects,” in IEEE/RSJ Int. Conf. Intell. Robots Syst., 2018, pp. 4007–4013.
  23. M. Takizawa, S. Kudoh, and T. Suehiro, “Implementation of twisting skill to robot hands for manipulating linear deformable objects,” in IEEE/RSJ Int. Conf. Intell. Robots Syst., 2016, pp. 945–950.
  24. S. Zhaole, J. Zhu, and R. B. Fisher, “DexDLO: Learning goal-conditioned dexterous policy for dynamic manipulation of deformable linear objects,” in IEEE Int. Conf. Robot. Autom., 2024.
  25. GelSight Mini. [Online]. Available: https://www.gelsight.com/gelsightmini/
  26. P. Beeson and B. Ames, “TRAC-IK: An open-source library for improved solving of generic inverse kinematics,” in IEEE-RAS Int. Conf. Human. Robots, 2015, pp. 928–935.
  27. S. Qiu and M. R. Kermani, “Precision grasp using an arm-hand system as a hybrid parallel-serial system: A novel inverse kinematics solution,” IEEE Robot. Autom. Lett., vol. 6, no. 4, pp. 8530–8536, 2021.
  28. D. Kraft, “A software package for sequential quadratic programming,” DLR German Aerospace Center — Institute for Flight Mechanics, Koln, Germany, Tech. Rep. DFVLR-FB 88-28, 1988.
  29. T. Wimbock, C. Ott, and G. Hirzinger, “Impedance behaviors for two-handed manipulation: Design and experiments,” in IEEE Int. Conf. Robot. Autom., 2007, pp. 4182–4189.
  30. T. Yoshikawa, “Multifingered robot hands: Control for grasping and manipulation,” Annu. Rev. Control, vol. 34, no. 2, pp. 199–208, 2010.
  31. S. R. Buss, “Introduction to inverse kinematics with jacobian transpose, pseudoinverse and damped least squares methods,” IEEE J. Robot. Autom., vol. 17, no. 1-19, p. 16, 2004.
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