journal article·2026-03Open access

Biohybrid Tendons Enhance the Power‐to‐Weight Ratio and Modularity of Muscle‐Powered Robots

Nicolas Castro, Ronald Heisser, Maheera Bawa, Bastien Aymon, Sarah Wu, Annika Marschner, Sonika Kohli, Angel Bu, Laura Rosado, Martin Culpepper, Xuanhe Zhao, Ritu Raman

Advanced Science· Volume 13· Issue 15

Abstract

Abstract Biohybrid robots powered by tissue engineered skeletal muscle have historically relied on architectures in which muscle actuators are placed directly on skeletons, thus limiting the accessible design space for such machines. By contrast, native musculoskeletal architecture relies on tendons to bridge the interface between muscles and skeletons, enabling precise, space‐efficient, and energy‐efficient force transmission. In this study, a mathematical model of the muscle–tendon–skeleton interface is used to design a biohybrid muscle–tendon unit composed of tissue engineered muscle coupled to adhesive tough hydrogel tendons. It is demonstrated that tuning tendon stiffness and pre‐tension optimizes actuator performance, and tuning skeleton stiffness modulates force transmission from muscles to skeletons, with fatigue characteristics measured over > 7000 cycles. Furthermore, an ≈11X improvement in power‐to‐weight ratio of muscle–tendon units is demonstrated compared to previous demonstrations of robots powered by muscles alone. This work validates a robust approach for designing, manufacturing, and deploying muscle–tendon actuators that promises to enhance the modularity and efficiency of biohybrid robots.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Micro and Nano Robotics · Soft Robotics and Applications · Advanced Materials and Mechanics

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA 02472 USA

Funding

Army Research Office · National Science Foundation

References (65)

Show source-provided references
1. 10.1146/annurev-bioeng-110122-01380510.1146/annurev-bioeng-110122-013805 ↗
2. 10.1126/scirobotics.aaq049510.1126/scirobotics.aaq0495 ↗
3. 10.1088/1748-3190/ac9c3b10.1088/1748-3190/ac9c3b ↗
4. 10.1021/acs.chemrev.4c0078510.1021/acs.chemrev.4c00785 ↗
5. 10.1073/pnas.140157711110.1073/pnas.1401577111 ↗
6. 10.1073/pnas.151613911310.1073/pnas.1516139113 ↗
7. 10.1002/adfm.20180114510.1002/adfm.201801145 ↗
8. 10.1126/scirobotics.abe757710.1126/scirobotics.abe7577 ↗
9. 10.1016/j.matt.2023.12.03510.1016/j.matt.2023.12.035 ↗
10. 10.1073/pnas.191083711710.1073/pnas.1910837117 ↗
11. 10.1126/sciadv.adl154910.1126/sciadv.adl1549 ↗
12. 10.1126/science.114688510.1126/science.1146885 ↗
13. 10.1126/science.abh047410.1126/science.abh0474 ↗
14. 10.1038/ncomms408110.1038/ncomms4081 ↗
15. A.Balciunaite O.Yasa M.Filippi M. Y.Michelis R. K.Katzschmann in2024 IEEE 7th International Conference on Soft Robotics (RoboSoft) IEEE IEEE San Diego CA USA 2024 pp.525–530.10.1109/RoboSoft60065.2024.10522009 ↗
16. 10.1002/adfm.20220267410.1002/adfm.202202674 ↗
17. 10.1002/smll.20190000610.1002/smll.201900006 ↗
18. 10.1126/scirobotics.adr647210.1126/scirobotics.adr6472 ↗
19. 10.1073/pnas.181768211610.1073/pnas.1817682116 ↗
20. 10.1088/1758-5090/ac4d1910.1088/1758-5090/ac4d19 ↗
21. 10.1016/j.biomaterials.2022.12164310.1016/j.biomaterials.2022.121643 ↗
22. 10.1039/B612082B10.1039/B612082B ↗
23. 10.1126/scirobotics.aat444010.1126/scirobotics.aat4440 ↗
24. 10.1089/ten.tea.2012.042110.1089/ten.tea.2012.0421 ↗
25. 10.1126/scirobotics.adr551210.1126/scirobotics.adr5512 ↗
26. 10.1002/aisy.20240098910.1002/aisy.202400989 ↗
27. 10.1002/adbi.20160003510.1002/adbi.201600035 ↗
28. 10.1002/aisy.20200025310.1002/aisy.202000253 ↗
29. 10.1002/aisy.20220035610.1002/aisy.202200356 ↗
30. 10.1126/scirobotics.add105310.1126/scirobotics.add1053 ↗
31. 10.1126/scirobotics.ado005110.1126/scirobotics.ado0051 ↗
32. 10.1073/pnas.210007711810.1073/pnas.2100077118 ↗
33. 10.1002/aisy.20240041310.1002/aisy.202400413 ↗
34. 10.1126/scirobotics.adr929910.1126/scirobotics.adr9299 ↗
35. 10.1002/adhm.20190113710.1002/adhm.201901137 ↗
36. 10.1073/pnas.190705111610.1073/pnas.1907051116 ↗
37. 10.1002/admt.20180063110.1002/admt.201800631 ↗
38. 10.1002/adhm.20170003010.1002/adhm.201700030 ↗
39. 10.1002/adfm.20241033410.1002/adfm.202410334 ↗
40. 10.1126/scirobotics.abf157110.1126/scirobotics.abf1571 ↗
41. 10.1002/adhm.20240184410.1002/adhm.202401844 ↗
42. 10.1039/D4BM01017E10.1039/D4BM01017E ↗
43. McCuller C. · 2023
44. 10.1007/s00223-014-9915-y10.1007/s00223-014-9915-y ↗
45. 10.1002/aisy.20230083410.1002/aisy.202300834 ↗
46. 10.1016/S0021-9290(98)00189-410.1016/S0021-9290(98)00189-4 ↗
47. 10.1101/cshperspect.a04150010.1101/cshperspect.a041500 ↗
48. 10.1007/s13770-019-00196-w10.1007/s13770-019-00196-w ↗
49. 10.1016/j.jmbbm.2009.03.00410.1016/j.jmbbm.2009.03.004 ↗
50. 10.1002/mus.2209410.1002/mus.22094 ↗
51. 10.1016/0021-9290(93)90042-D10.1016/0021-9290(93)90042-D ↗
52. 10.1038/nprot.2016.18510.1038/nprot.2016.185 ↗
53. 10.1016/j.cobme.2024.10053210.1016/j.cobme.2024.100532 ↗
54. 10.1126/sciadv.adv262810.1126/sciadv.adv2628 ↗
55. 10.1021/acsabm.3c0021510.1021/acsabm.3c00215 ↗
56. 10.1063/5.018922110.1063/5.0189221 ↗
57. 10.1126/scitranslmed.abh285710.1126/scitranslmed.abh2857 ↗
58. 10.1002/wdev.23010.1002/wdev.230 ↗
59. R. H.Heisser A.Bu L.Schwendeman T.Rossy R.Raman bioRxiv2025 https://doi.org/10.1101/2025.06.02.657505.10.1101/2025.06.02.657505 ↗
60. 10.1016/j.actbio.2022.12.02810.1016/j.actbio.2022.12.028 ↗
61. 10.1016/j.actbio.2022.08.03710.1016/j.actbio.2022.08.037 ↗
62. 10.1002/advs.20240362210.1002/advs.202403622 ↗
63. 10.1038/s41467-023-36371-w10.1038/s41467-023-36371-w ↗
64. 10.1007/s10974‐025‐09696‐w10.1007/s10974‐025‐09696‐w ↗
65. Mestre R. · Living Mach. · 2019 · 205