Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
139 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Tactile-Reactive Roller Grasper (2306.09946v1)

Published 16 Jun 2023 in cs.RO

Abstract: Manipulation of objects within a robot's hand is one of the most important challenges in achieving robot dexterity. The "Roller Graspers" refers to a family of non-anthropomorphic hands utilizing motorized, rolling fingertips to achieve in-hand manipulation. These graspers manipulate grasped objects by commanding the rollers to exert forces that propel the object in the desired motion directions. In this paper, we explore the possibility of robot in-hand manipulation through tactile-guided rolling. We do so by developing the Tactile-Reactive Roller Grasper (TRRG), which incorporates camera-based tactile sensing with compliant, steerable cylindrical fingertips, with accompanying sensor information processing and control strategies. We demonstrated that the combination of tactile feedback and the actively rolling surfaces enables a variety of robust in-hand manipulation applications. In addition, we also demonstrated object reconstruction techniques using tactile-guided rolling. A controlled experiment was conducted to provide insights on the benefits of tactile-reactive rollers for manipulation. We considered two manipulation cases: when the fingers are manipulating purely through rolling and when they are periodically breaking and reestablishing contact as in regrasping. We found that tactile-guided rolling can improve the manipulation robustness by allowing the grasper to perform necessary fine grip adjustments in both manipulation cases, indicating that hybrid rolling fingertip and finger-gaiting designs may be a promising research direction.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (52)
  1. I. M. Bullock and A. M. Dollar, “Classifying human manipulation behavior,” in 2011 IEEE International Conference on Rehabilitation Robotics.   IEEE, 2011, pp. 1–6.
  2. S. R. Company, “Shadow dexterous hand,” https://www.shadowrobot.com/dexterous-hand-series/, accessed: 2022-03-10.
  3. T. Chen, J. Xu, and P. Agrawal, “A system for general in-hand object re-orientation,” in Conference on Robot Learning.   PMLR, 2022, pp. 297–307.
  4. M. Bjelonic, C. D. Bellicoso, Y. de Viragh, D. Sako, F. D. Tresoldi, F. Jenelten, and M. Hutter, “Keep rollin’—whole-body motion control and planning for wheeled quadrupedal robots,” IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 2116–2123, 2019.
  5. S. Yuan, A. D. Epps, J. B. Nowak, and J. K. Salisbury, “Design of a roller-based dexterous hand for object grasping and within-hand manipulation,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 8870–8876.
  6. S. Yuan, L. Shao, C. L. Yako, A. Gruebele, and J. K. Salisbury, “Design and control of roller grasper v2 for in-hand manipulation,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, pp. 9151–9158.
  7. H. Yousef, M. Boukallel, and K. Althoefer, “Tactile sensing for dexterous in-hand manipulation in robotics—a review,” Sensors and Actuators A: physical, vol. 167, no. 2, pp. 171–187, 2011.
  8. N. Fazeli, M. Oller, J. Wu, Z. Wu, J. B. Tenenbaum, and A. Rodriguez, “See, feel, act: Hierarchical learning for complex manipulation skills with multisensory fusion,” Science Robotics, vol. 4, no. 26, p. eaav3123, 2019.
  9. Y. She, S. Wang, S. Dong, N. Sunil, A. Rodriguez, and E. Adelson, “Cable manipulation with a tactile-reactive gripper,” The International Journal of Robotics Research, vol. 40, no. 12-14, pp. 1385–1401, 2021. [Online]. Available: https://doi.org/10.1177/02783649211027233
  10. C. Piazza, G. Grioli, M. Catalano, and A. Bicchi, “A century of robotic hands,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 2, pp. 1–32, 2019.
  11. M. A. Diftler, J. Mehling, M. E. Abdallah, N. A. Radford, L. B. Bridgwater, A. M. Sanders, R. S. Askew, D. M. Linn, J. D. Yamokoski, F. Permenter et al., “Robonaut 2-the first humanoid robot in space,” in 2011 IEEE international conference on robotics and automation.   IEEE, 2011, pp. 2178–2183.
  12. M. Grebenstein, A. Albu-Schäffer, T. Bahls, M. Chalon, O. Eiberger, W. Friedl, R. Gruber, S. Haddadin, U. Hagn, R. Haslinger et al., “The dlr hand arm system,” in 2011 IEEE International Conference on Robotics and Automation.   IEEE, 2011, pp. 3175–3182.
  13. J. Salisbury, “Kinematics and force analysis of articulated hands, phd thesis,” 1982.
  14. S. C. Jacobsen, J. E. Wood, D. Knutti, and K. B. Biggers, “The utah/mit dextrous hand: Work in progress,” The International Journal of Robotics Research, vol. 3, no. 4, pp. 21–50, 1984.
  15. R. R. Ma and A. M. Dollar, “An underactuated hand for efficient finger-gaiting-based dexterous manipulation,” in 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014).   IEEE, 2014, pp. 2214–2219.
  16. W. G. Bircher, A. S. Morgan, K. Hang, and A. M. Dollar, “Energy gradient-based graphs for planning within-hand caging manipulation,” in 2019 International Conference on Robotics and Automation (ICRA).   IEEE, 2019, pp. 2462–2467.
  17. R. R. Ma, N. Rojas, and A. M. Dollar, “Spherical hands: Toward underactuated, in-hand manipulation invariant to object size and grasrp location,” Journal of Mechanisms and Robotics, vol. 8, no. 6, p. 061021, 2016.
  18. C. M. McCann and A. M. Dollar, “Design of a stewart platform-inspired dexterous hand for 6-dof within-hand manipulation,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2017, pp. 1158–1163.
  19. P. Datseris and W. Palm, “Principles on the Development of Mechanical Hands Which Can Manipulate Objects by Means of Active Control,” Journal of Mechanical Design, vol. 107, no. 2, pp. 148–156, 06 1985. [Online]. Available: https://doi.org/10.1115/1.3258703
  20. N. Govindan and A. Thondiyath, “Design and analysis of a multimodal grasper having shape conformity and within-hand manipulation with adjustable contact forces,” Journal of Mechanisms and Robotics, vol. 11, no. 5, 2019.
  21. V. Tincani, M. G. Catalano, E. Farnioli, M. Garabini, G. Grioli, G. Fantoni, and A. Bicchi, “Velvet fingers: A dexterous gripper with active surfaces,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2012, pp. 1257–1263.
  22. R. R. Ma and A. M. Dollar, “In-hand manipulation primitives for a minimal, underactuated gripper with active surfaces,” in International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol. 50152.   American Society of Mechanical Engineers, 2016, p. V05AT07A072.
  23. A. Kakogawa, H. Nishimura, and S. Ma, “Underactuated modular finger with pull-in mechanism for a robotic gripper,” in 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO).   IEEE, 2016, pp. 556–561.
  24. B. Ward-Cherrier, N. Pestell, L. Cramphorn, B. Winstone, M. E. Giannaccini, J. Rossiter, and N. F. Lepora, “The tactip family: Soft optical tactile sensors with 3d-printed biomimetic morphologies,” Soft robotics, vol. 5, no. 2, pp. 216–227, 2018.
  25. A. Alspach, K. Hashimoto, N. Kuppuswarny, and R. Tedrake, “Soft-bubble: A highly compliant dense geometry tactile sensor for robot manipulation,” in 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft).   IEEE, 2019, pp. 597–604.
  26. C. Sferrazza, T. Bi, and R. D’Andrea, “Learning the sense of touch in simulation: A sim-to-real strategy for vision-based tactile sensing.”   IEEE Press, 2020, p. 4389–4396. [Online]. Available: https://doi.org/10.1109/IROS45743.2020.9341285
  27. A. Yamaguchi and C. G. Atkeson, “Combining finger vision and optical tactile sensing: Reducing and handling errors while cutting vegetables,” in 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids).   IEEE, 2016, pp. 1045–1051.
  28. M. Lambeta, P.-W. Chou, S. Tian, B. Yang, B. Maloon, V. R. Most, D. Stroud, R. Santos, A. Byagowi, G. Kammerer et al., “Digit: A novel design for a low-cost compact high-resolution tactile sensor with application to in-hand manipulation,” IEEE Robotics and Automation Letters, vol. 5, no. 3, pp. 3838–3845, 2020.
  29. A. Padmanabha, F. Ebert, S. Tian, R. Calandra, C. Finn, and S. Levine, “Omnitact: A multi-directional high resolution touch sensor,” arXiv preprint arXiv:2003.06965, 2020.
  30. B. Romero, F. Veiga, and E. Adelson, “Soft, round, high resolution tactile fingertip sensors for dexterous robotic manipulation,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 4796–4802.
  31. R. S. Dahiya, G. Metta, M. Valle, and G. Sandini, “Tactile sensing—from humans to humanoids,” IEEE transactions on robotics, vol. 26, no. 1, pp. 1–20, 2009.
  32. M. R. Cutkosky and W. Provancher, “Force and tactile sensing,” in Springer Handbook of Robotics.   Springer, 2016, pp. 717–736.
  33. K. Shimonomura, “Tactile image sensors employing camera: A review,” Sensors, vol. 19, no. 18, p. 3933, 2019.
  34. G. Cao, J. Jiang, C. Lu, D. F. Gomes, and S. Luo, “Touchroller: A rolling optical tactile sensor for rapid assessment of large surfaces,” arXiv preprint arXiv:2103.00595, 2021.
  35. 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.
  36. A. Amini, J. I. Lipton, and D. Rus, “Uncertainty aware texture classification and mapping using soft tactile sensors,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2020, pp. 4249–4256.
  37. B. Fang, X. Long, F. Sun, H. Liu, S. Zhang, and C. Fang, “Tactile-based fabric defect detection using convolutional neural network with attention mechanism,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–9, 2022.
  38. J. Jiang, G. Cao, D. F. Gomes, and S. Luo, “Vision-guided active tactile perception for crack detection and reconstruction,” in 2021 29th Mediterranean Conference on Control and Automation (MED).   IEEE, 2021, pp. 930–936.
  39. M. Björkman, Y. Bekiroglu, V. Högman, and D. Kragic, “Enhancing visual perception of shape through tactile glances,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2013, pp. 3180–3186.
  40. S. Wang, J. Wu, X. Sun, W. Yuan, W. T. Freeman, J. B. Tenenbaum, and E. H. Adelson, “3d shape perception from monocular vision, touch, and shape priors,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2018, pp. 1606–1613.
  41. E. Smith, R. Calandra, A. Romero, G. Gkioxari, D. Meger, J. Malik, and M. Drozdzal, “3d shape reconstruction from vision and touch,” Advances in Neural Information Processing Systems, vol. 33, pp. 14 193–14 206, 2020.
  42. S. Wang, Y. She, B. Romero, and E. Adelson, “Gelsight wedge: Measuring high-resolution 3d contact geometry with a compact robot finger,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 6468–6475.
  43. G. Bradski, “The opencv library.” Dr. Dobb’s Journal: Software Tools for the Professional Programmer, vol. 25, no. 11, pp. 120–123, 2000.
  44. J. Doerner, “Fast poisson reconstruction in python,” https://gist.github.com/jackdoerner/b9b5e62a4c3893c76e4c, 2014.
  45. C. Sferrazza and R. D’Andrea, “Design, motivation and evaluation of a full-resolution optical tactile sensor,” Sensors, vol. 19, no. 4, p. 928, 2019.
  46. G. Zhang, Y. Du, H. Yu, and M. Y. Wang, “Deltact: A vision-based tactile sensor using a dense color pattern,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 10 778–10 785, 2022.
  47. B. K. Horn and B. G. Schunck, “Determining optical flow,” Artificial intelligence, vol. 17, no. 1-3, pp. 185–203, 1981.
  48. D. J. Montana, “The kinematics of contact and grasp,” The International Journal of Robotics Research, vol. 7, no. 3, pp. 17–32, 1988.
  49. Y. She, S. Wang, S. Dong, N. Sunil, A. Rodriguez, and E. Adelson, “Cable manipulation with a tactile-reactive gripper,” The International Journal of Robotics Research, vol. 40, no. 12-14, pp. 1385–1401, 2021.
  50. F. Veiga, H. Van Hoof, J. Peters, and T. Hermans, “Stabilizing novel objects by learning to predict tactile slip,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2015, pp. 5065–5072.
  51. W. Chen, H. Khamis, I. Birznieks, N. F. Lepora, and S. J. Redmond, “Tactile sensors for friction estimation and incipient slip detection—toward dexterous robotic manipulation: A review,” IEEE Sensors Journal, vol. 18, no. 22, pp. 9049–9064, 2018.
  52. N. Kuppuswamy, A. Alspach, A. Uttamchandani, S. Creasey, T. Ikeda, and R. Tedrake, “Soft-bubble grippers for robust and perceptive manipulation,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2020, pp. 9917–9924.
Citations (3)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

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