Abstract
Manipulation of objects of variable size, shape and surface properties remains a challenging problem in robotics. In this paper, we present the design of a soft, pneumatically variable contact stiffness grasper and the training of a sparse, bioinspired neural network controller for pick-and-place manipulation. Both the soft grasper and the neural network controller are inspired by the sea slug Aplysia californica. The compliant nature of the grasper is beneficial for maintaining rich contact with objects, which simplifies the control problem. Adopting biologically inspired neural dynamics and network structure has the further advantage of building neural network controllers that are robust and efficient for real-time control. To verify the effectiveness of our bio-inspired approach for object grasping and manipulation, we developed a simulation environment that reflects the compliance between the soft grasper and the object. We demonstrate that when integrated with the neural network controller, the grasper successfully completed the pick-and-place task in simulation. With minimal tuning, the controller was then successfully transferred to the physical soft grasping platform and was able to successfully pick-and-place objects of various size and mass, up to a maximum tested mass of 706 g. The bio-inspired approach to both the morphology and the control of the soft-grasper presented here thus represents an exciting first step toward the robust adaptive manipulation of a broad class of objects.
This work was supported in part by the National Science Foundation (NSF) grant no. FRR-2138873 and by a GEM fellowship. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.
R. Sukhnandan and Y. Li—These authors contributed equally to the work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Although we can achieve variable stiffness through active pressure control, we set the pressure applied to the soft jaws as a constant in this work. The regulation of the stiffness is treated as future work (see Sect. 4)
References
Becker, K., et al.: Active entanglement enables stochastic, topological grasping. Proc. Natl. Acad. Sci. 119(42), e2209819119 (2022)
Ciocarlie, M., Miller, A., Allen, P.: Grasp analysis using deformable fingers. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4122–4128, August 2005. ISSN: 2153-0866
Coulson, R., Stabile, C.J., Turner, K.T., Majidi, C.: Versatile soft robot gripper enabled by stiffness and adhesion tuning via thermoplastic composite. Soft Robot. 9(2), 189–200 (2022)
Coumans, E., Bai, Y.: Pybullet, a python module for physics simulation for games, robotics and machine learning (2016)
Dai, K., et al.: SLUGBOT, an \(Aplysia\)-inspired robotic grasper for studying control. In: Hunt, A., et al. (eds.) Biomimetic and Biohybrid Systems. LNAI, vol. 13548, pp. 182–194. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20470-8_19
Gill, J.P., Chiel, H.J.: Rapid adaptation to changing mechanical load by ordered recruitment of identified motor neurons. eNeuro 7(3) (2020). ENEURO.0016-20.2020
Hasani, R., Lechner, M., Amini, A., Rus, D., Grosu, R.: Liquid time-constant networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 7657–7666 (2021)
Hilts, W.W., Szczecinski, N.S., Quinn, R.D., Hunt, A.J.: A dynamic neural network designed using analytical methods produces dynamic control properties similar to an analogous classical controller. IEEE Control Syst. Lett. 3(2), 320–325 (2018)
Hunt, A., Szczecinski, N., Quinn, R.: Development and training of a neural controller for hind leg walking in a dog robot. Front. Neurorobot. 11, 18 (2017)
Hurwitz, I., Susswein, A.J.: Adaptation of feeding sequences in \(Aplysia\,oculifera\) to changes in the load and width of food. J. Exp. Biol. 166(1), 215–235 (1992)
Jayakumar, S.M., et al.: Multiplicative interactions and where to find them. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, 26–30 April 2020. OpenReview.net (2020)
Jing, J., Weiss, K.R.: Neural mechanisms of motor program switching in \(Aplysia\). J. Neurosci. 21(18), 7349–7362 (2001)
Jing, J., Weiss, K.R.: Generation of variants of a motor act in a modular and hierarchical motor network. Curr. Biol. 15(19), 1712–1721 (2005)
Kehl, C.E., et al.: Soft-surface grasping: radular opening in \(Aplysia\, californica\). J. Exp. Biol. 222(16), jeb191254 (2019)
Koch, C.: Biophysics of Computation: Information Processing in Single Neurons. Oxford University Press, Oxford (1998)
Kuppuswamy, N., Alspach, A., Uttamchandani, A., Creasey, S., Ikeda, T., Tedrake, R.: Soft-bubble grippers for robust and perceptive manipulation. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9917–9924 (2020)
Lechner, M., Hasani, R., Amini, A., Henzinger, T.A., Rus, D., Grosu, R.: Neural circuit policies enabling auditable autonomy. Nat. Mach. Intell. 2(10), 642–652 (2020)
Li, Y., Sukhnandan, R., Gill, J.P., Chiel, H.J., Webster-Wood, V., Quinn, R.D: A bioinspired synthetic nervous system controller for pick-and-place manipulation. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 8047–8053 (2023)
Lynch, K.M., Park, F.C.: Modern Robotics: Mechanics, Planning, and Control. Cambridge University Press, Cambridge (2017)
Majidi, C.: Soft-matter engineering for soft robotics. Adv. Mater. Technol. 4(2), 1800477 (2019)
Mangan, E.V., Kingsley, D.A., Quinn, R.D., Sutton, G.P., Mansour, J.M., Chiel, H.J.: A biologically inspired gripping device. Ind. Robot. 32(1), 49–54 (2005)
Nishimura, T., Suzuki, Y., Tsuji, T., Watanabe, T.: Fluid pressure monitoring-based strategy for delicate grasping of fragile objects by a robotic hand with fluid fingertips. Sensors 19(4), 782 (2019)
Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32, pp. 8024–8035. Curran Associates, Inc. (2019)
Peters, J., et al.: Actuation and stiffening in fluid-driven soft robots using low-melting-point material. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, pp. 4692–4698. IEEE, November 2019
Root, S.E., et al.: Bio-inspired design of soft mechanisms using a toroidal hydrostat. Cell Rep. Phys. Sci. 2(9), 100572 (2021)
Roth, F.L., Driscoll, R.L., Holt, W.L.: Frictional properties of rubber. Rubber Chem. Technol. 16(1), 155–177 (1943). https://doi.org/10.5254/1.3540095
Shintake, J., Cacucciolo, V., Floreano, D., Shea, H.: Soft robotic grippers. Adv. Mater. 30(29), 1707035 (2018)
Shintake, J., Schubert, B., Rosset, S., Shea, H., Floreano, D.: Variable stiffness actuator for soft robotics using dielectric elastomer and low-melting-point alloy. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1097–1102, September 2015
Suh, H.T., Kuppuswamy, N., Pang, T., Mitiguy, P., Alspach, A., Tedrake, R.: SEED: series elastic end effectors in 6D for visuotactile tool use. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4684–4691 (2022)
Szczecinski, N.S., Hunt, A.J., Quinn, R.D.: Design process and tools for dynamic neuromechanical models and robot controllers. Biol. Cybern. 111(1), 105–127 (2017). https://doi.org/10.1007/s00422-017-0711-4
Szczecinski, N.S., Hunt, A.J., Quinn, R.D.: A functional subnetwork approach to designing synthetic nervous systems that control legged robot locomotion. Front. Neurorobot. 11, 37 (2017)
Szczecinski, N.S., Quinn, R.D.: Template for the neural control of directed stepping generalized to all legs of MantisBot. Bioinspiration Biomimetics 12(4), 045001 (2017)
Wang, J., Chortos, A.: Control strategies for soft robot systems. Adv. Intell. Syst. 4(5), 2100165 (2022)
Webster-wood, V.A., Gill, J.P., Thomas, P.J., Chiel, H.J.: Control for multifunctionality: bioinspired control based on feeding in \(Aplysia\,californica\). Biol. Cybern. 114(6), 557–588 (2020). https://doi.org/10.1007/s00422-020-00851-9
Werbos, P.: Backpropagation through time: what it does and how to do it. Proc. IEEE 78(10), 1550–1560 (1990)
Yoder, Z., Macari, D., Kleinwaks, G., Schmidt, I., Acome, E., Keplinger, C.: A soft, fast and versatile electrohydraulic gripper with capacitive object size detection. Adv. Funct. Mater. 33(3), 2209080 (2022)
Zhang, B., Xie, Y., Zhou, J., Wang, K., Zhang, Z.: State-of-the-art robotic grippers, grasping and control strategies, as well as their applications in agricultural robots: a review. Comput. Electron. Agric. 177, 105694 (2020)
Acknowledgements
The authors would like to thank Ashlee Liao, Saul Schaffer, and Avery Williamson for their helpful comments in editing this manuscript.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sukhnandan, R. et al. (2023). Synthetic Nervous System Control of a Bioinspired Soft Grasper for Pick-and-Place Manipulation. In: Meder, F., Hunt, A., Margheri, L., Mura, A., Mazzolai, B. (eds) Biomimetic and Biohybrid Systems. Living Machines 2023. Lecture Notes in Computer Science(), vol 14157. Springer, Cham. https://doi.org/10.1007/978-3-031-38857-6_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-38857-6_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-38856-9
Online ISBN: 978-3-031-38857-6
eBook Packages: Computer ScienceComputer Science (R0)