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Published Online: 2 June 2020

MakeSense: Automated Sensor Design for Proprioceptive Soft Robots

Publication: Soft Robotics
Volume 7, Issue Number 3


Soft robots have applications in safe human–robot interactions, manipulation of fragile objects, and locomotion in challenging and unstructured environments. In this article, we present a computational method for augmenting soft robots with proprioceptive sensing capabilities. Our method automatically computes a minimal stretch-receptive sensor network to user-provided soft robotic designs, which is optimized to perform well under a set of user-specified deformation-force pairs. The sensorized robots are able to reconstruct their full deformation state, under interaction forces. We cast our sensor design as a subselection problem, selecting a minimal set of sensors from a large set of fabricable ones, which minimizes the error when sensing specified deformation-force pairs. Unique to our approach is the use of an analytical gradient of our reconstruction performance measure with respect to selection variables. We demonstrate our technique on a bending bar and gripper example, illustrating more complex designs with a simulated tentacle.

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1. Park Y-L, Majidi C, Kramer R, et al. Hyperelastic pressure sensing with a liquid-embedded elastomer. J Micromech Microeng 2010;20:125029.
2. O'Brien B, Gisby T, Anderson IA. Stretch sensors for human body motion. In: Electroactive Polymer Actuators and Devices (EAPAD) 2014, vol. 9056. Bellingham, WA: International Society for Optics and Photonics, 2014, p. 905618.
3. Mengüç Y, Park Y-L, Pei H, et al. Wearable soft sensing suit for human gait measurement. Int J Robot Res 2014;33:1748–1764.
4. Culha U, Nurzaman SG, Clemens F, et al. Svas3: strain vector aided sensorization of soft structures. Sensors 2014;14:12748–12770.
5. Wall V, Zöller G, Brock O. A method for sensorizing soft actuators and its application to the RBO hand 2. In: 2017 IEEE International Conference on Robotics and Automation (ICRA). Piscataway, NJ: IEEE, 2017, pp. 4965–4970.
6. Yuen MC, Kramer-Bottiglio R, Paik J. Strain sensor-embedded soft pneumatic actuators for extension and bending feedback. In: 2018 IEEE International Conference on Soft Robotics (RoboSoft). Piscataway, NJ: IEEE, 2018, pp. 202–207.
7. Suzumori K, Iikura S, Tanaka H. Development of flexible microactuator and its applications to robotic mechanisms. In: Proceedings. 1991 IEEE International Conference on Robotics and Automation, 1991. Piscataway, NJ: IEEE, 1991, pp. 1622–1627.
8. Ilievski F, Mazzeo AD, Shepherd RF, et al. Soft robotics for chemists. Angew Chem 2011;123:1930–1935.
9. Connolly F, Walsh CJ, Bertoldi K. Automatic design of fiber-reinforced soft actuators for trajectory matching. Proc Natl Acad Sci U S A 2017;114:51–56.
10. Robertson MA, Paik J. New soft robots really suck: vacuum-powered systems empower diverse capabilities. Sci Robot 2017;2:eaan6357.
11. Miriyev A, Stack K, Lipson H. Soft material for soft actuators. Nat Commun 2017;8:596.
12. Umedachi T, Vikas V, Trimmer BA. Highly deformable 3-D printed soft robot generating inching and crawling locomotions with variable friction legs. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway, NJ: IEEE, 2013, pp. 4590–4595.
13. Peele BN, Wallin TJ, Zhao H, et al. 3D printing antagonistic systems of artificial muscle using projection stereolithography. Bioinspiration Biomimetics 2015;10:055003.
14. Morrow J, Hemleben S, Menguc Y. Directly fabricating soft robotic actuators with an open-source 3-D printer. IEEE Robot Autom Lett 2017;2:277–281.
15. Drotman D, Ishida M, Jadhav S, et al. Application-driven design of soft, 3D printed, pneumatic actuators with bellows. IEEE/ASME Trans Mechatron 2018;24:78–87.
16. Marchese AD, Onal CD, Rus D. Autonomous soft robotic fish capable of escape maneuvers using fluidic elastomer actuators. Soft Robot 2014;1:75–87, .
17. McEvoy MA, Correll N. Materials that couple sensing, actuation, computation, and communication. Science 2015;347:1261689.
18. Wehner M, Truby RL, Fitzgerald DJ, et al. An integrated design and fabrication strategy for entirely soft, autonomous robots. Nature 2016;536:451–455.
19. Shintake J, Cacucciolo V, Floreano D, et al. Soft robotic grippers. Adv Mater 2018;1707035.
20. Galloway KC, Becker KP, Phillips B, et al. Soft robotic grippers for biological sampling on deep reefs. Soft Robot 2016;3:23–33.
21. Hawkes EW, Blumenschein LH, Greer JD, et al. A soft robot that navigates its environment through growth. Sci Robot 2017;2:eaan3028.
22. Estrada MA, Mintchev S, Christensen DL, et al. Forceful manipulation with micro air vehicles. Sci Robot 2018;3:eaau6903.
23. Cianchetti M, Ranzani T, Gerboni G, et al. Soft robotics technologies to address shortcomings in today's minimally invasive surgery: the stiff-flop approach. Soft Robot 2014;1:122–131.
24. Deimel R, Brock O. A novel type of compliant and underactuated robotic hand for dexterous grasping. Int J Robot Res 2016;35:161–185.
25. Polygerinos P, Wang Z, Galloway KC, et al. Soft robotic glove for combined assistance and at-home rehabilitation. Robot Auton Syst 2015;73:135–143.
26. Diteesawat RS, Helps T, Taghavi M, et al. High strength bubble artificial muscles for walking assistance. In: 2018 IEEE International Conference on Soft Robotics (RoboSoft). Piscataway, NJ: IEEE, 2018, pp. 388–393.
27. Mutlu R, Alici G, in het Panhuis M, Spinks GM. 3D printed flexure hinges for soft monolithic prosthetic fingers. Soft Robot 2016;3:120–133.
28. Hauser H, Ijspeert AJ, Füchslin RM, et al. Towards a theoretical foundation for morphological computation with compliant bodies. Biol Cybern 2011;105:355–370.
29. Michael-Titus A, Revest P, Shortland P. The Nervous System, 2nd ed. London, UK: Churchill Livingstone, 2010.
30. Wang H, Totaro M, Beccai L. Toward perceptive soft robots: progress and challenges. Adv Sci 2018:5;1800541.
31. Polygerinos P, Correll N, Morin SA, et al. Soft robotics: review of fluid-driven intrinsically soft devices; manufacturing, sensing, control, and applications in human-robot interaction. Adv Eng Mater 2017;19:1700016.
32. Bächer M, Hepp B, Pece F, et al. Defsense: computational design of customized deformable input devices. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, ser. CHI’16. New York, NY: ACM, 2016, pp. 3806–3816.
33. Park YL, Chen BR, Wood RJ. Design and fabrication of soft artificial skin using embedded microchannels and liquid conductors. IEEE Sens J 2012;12:2711–2718.
34. Farrow N, Correll N. A soft pneumatic actuator that can sense grasp and touch. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway, NJ: IEEE, 2015, pp. 2317–2323.
35. Case JC, White EL, Kramer RK. Sensor enabled closed-loop bending control of soft beams. Smart Mater Struct 2016;25:045018.
36. White EL, Case JC, Kramer RK. Multi-mode strain and curvature sensors for soft robotic applications. Sens Actuators A 2017;253:188–197.
37. Muth JT, Vogt DM, Truby RL, et al. Embedded 3D printing of strain sensors within highly stretchable elastomers. Adv Mater 2014;26:6307–6312.
38. Truby RL, Wehner M, Grosskopf AK, et al. Soft somatosensitive actuators via embedded 3D printing. Adv Mater 2018;30:1706383.
39. Chossat J-B, Park Y-L, Wood RJ, et al. A soft strain sensor based on ionic and metal liquids. IEEE Sens J 2013;13:3405–3414.
40. Larson C, Peele B, Li S, et al. Highly stretchable electroluminescent skin for optical signaling and tactile sensing. Science 2016;351:1071–1074.
41. Felt W, Suen M, Remy CD. Sensing the motion of bellows through changes in mutual inductance. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway, NJ: IEEE, 2016, pp. 5252–5257.
42. Tenzer Y, Jentoft LP, Howe RD. The feel of mems barometers: inexpensive and easily customized tactile array sensors. IEEE Robot Autom Mag 2014;3:89–95.
43. Zhang Y, Laput G, Harrison C. Electrick: low-cost touch sensing using electric field tomography. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. New York, NY: ACM, 2017, pp. 1–14.
44. Wall V, Zöller G, Brock O. Acoustic sensing for soft pneumatic actuators. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway, NJ: IEEE, 2018.
45. Chorley C, Melhuish C, Pipe T, et al. Development of a tactile sensor based on biologically inspired edge encoding. In: 2009. ICAR 2009. International Conference on Advanced Robotics. Piscataway, NJ: IEEE, 2009, pp. 1–6.
46. Zhang Z, Dequidt J, Duriez C. Vision-based sensing of external forces acting on soft robots using finite element method. IEEE Robot Autom Lett 2018;3:1529–1536.
47. Helps T, Rossiter J. Proprioceptive flexible fluidic actuators using conductive working fluids. Soft Robot 2018;5:175–189.
48. Coros S, Thomaszewski B, Noris G, et al. Computational design of mechanical characters. ACM Trans Graphics 2013;32:83:1–83.
49. Skouras M, Thomaszewski B, Coros S, et al. Computational design of actuated deformable characters. ACM Trans Graphics 2013;32:82:1–82.
50. Skouras M, Thomaszewski B, Kaufmann P, et al. Designing inflatable structures. ACM Trans Graphics 2014;33:63:1–63.
51. Megaro V, Zehnder J, Bächer M, et al. A computational design tool for compliant mechanisms. ACM Trans Graphics 2017;36:82:1–82.
52. Megaro V, Knoop E, Spielberg A, et al. Designing cable-driven actuation networks for kinematic chains and trees. In: Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, ser. SCA’17. New York, NY: ACM, 2017, pp. 15:1–15:10.
53. Pérez J, Thomaszewski B, Coros S, et al. Design and fabrication of flexible rod meshes. ACM Trans Graphics 2015;34:138:1–138.
54. Sifakis E, Barbic J. FEM simulation of 3D deformable solids: a practitioner's guide to theory, discretization and model reduction. In: ACM SIGGRAPH 2012 Courses, ser. SIGGRAPH’12. New York, NY: ACM, 2012, pp. 20:1–20:50.
55. Pozzi M, Miguel E, Deimel R, et al. Efficient FEM-based simulation of soft robots modeled as kinematic chains. In: IEEE International Conference on Robotics and Automation 2018. Piscataway, NJ: IEEE, 2018.
56. Nesterov Y. A method of solving a convex programming problem with convergence rate o (1/k2). Soviet Math Dokl 1983;27:372–376.
57. O'Donoghue B, Candès E. Adaptive restart for accelerated gradient schemes. Found Comput Math 2015;15:715–732.
58. Goh G. Why momentum really works. Distill, 2017. Available at: (accessed November 27, 2019).
59. Kim S, Oh J, Jeong D, et al. Consistent and reproducible direct ink writing of eutectic gallium–indium for high-quality soft sensors. Soft Robot 2018;5:601–612.
60. Pereira T, Rusinkiewicz S, Matusik W. Computational light routing: 3D printed optical fibers for sensing and display. ACM Trans Graphics 2014;33:24:1–24.
61. Pérez P, Gangnet M, Blake A. Poisson image editing. ACM Trans Graphics 2003;22:313–318.
62. Pham T-H, Kheddar A, Qammaz A, et al. Towards force sensing from vision: observing hand-object interactions to infer manipulation forces. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ: IEEE, 2015, pp. 2810–2819.
63. Schumacher C, Zehnder J, Bächer M. Set-In-Stone: worst-case optimization of structures weak in tension. ACM Trans Graphics 2018;37:252:1–252.
64. Bächer M, Whiting E, Bickel B, et al. Spin-it: optimizing moment of inertia for spinnable objects. ACM Trans Graphics 2014;33:96:1–96.

Information & Authors


Published In

cover image Soft Robotics
Soft Robotics
Volume 7Issue Number 3June 2020
Pages: 332 - 345
PubMed: 31891526


Published online: 2 June 2020
Published in print: June 2020
Published ahead of print: 9 December 2019


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Javier Tapia
Disney Research, Zurich, Switzerland.
Department of Computer Science, Universidad Rey Juan Carlos, Madrid, Spain.
Espen Knoop
Disney Research, Zurich, Switzerland.
Mojmir Mutný
Disney Research, Zurich, Switzerland.
Miguel A. Otaduy
Department of Computer Science, Universidad Rey Juan Carlos, Madrid, Spain.
Moritz Bächer [email protected]
Disney Research, Zurich, Switzerland.


Address correspondence to: Moritz Bächer, Disney Research, Stampfenbachstrasse 48, 8006 Zurich, Switzerland [email protected]

Author Disclosure Statement

No competing financial interests exist.

Funding Information

This work has been supported by the projects SOMA (European Commission, Horizon 2020 Framework Programme, H2020-ICT-645599) and TouchDesign (ERC Consolidator Grant No. 772738).

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