pith. sign in

arxiv: 2606.13601 · v1 · pith:7DGOA7BZnew · submitted 2026-06-11 · 💻 cs.RO · cs.SY· eess.SY

MCR-Bionic Hand: Anatomical Structural Priors for Dexterous Manipulation

Haosen Yang , Guowu Wei This is my paper

Pith reviewed 2026-06-27 06:23 UTC · model grok-4.3

classification 💻 cs.RO cs.SYeess.SY
keywords dexterous manipulationbiomimetic roboticsrobotic handanatomical structurestructural priorsmusculoskeletal designcontact-rich tasksbionic hand
0
0 comments X

The pith

Copying specific human hand anatomy lets a robotic hand generate grasps and modulate forces through mechanical structure alone.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper argues that human hand dexterity is partly built into physical architecture such as tendon routings, ligaments, and extensor mechanisms rather than supplied entirely by active control. It distinguishes structural prior generation, where wrist posture and specific tendon paths create default multi-joint configurations, from muscle-mediated modulation, where intrinsic pathways adjust stability and fingertip forces once contact occurs. The MCR-Bionic Hand is constructed as a one-to-one copy incorporating an eight-bone wrist, anatomical flexor routing, volar plates, collateral ligaments, and the dorsal extensor hood. Demonstrations in coin rotation, pen transfer, and cube manipulation show these features link low-dimensional inputs to coordinated action. A reader would care because the approach reduces the control burden on algorithms by embedding part of the intelligence in hardware geometry.

Core claim

The paper claims that anatomical biomimetics identifies human hand structures that perform part of control, with the MCR-Bionic Hand showing that wrist posture induces multi-joint pre-shaping, the extensor hood maps PIP posture to a coupled DIP response, and intrinsic plus pathways modulate distal stability and fingertip action direction after grasp formation, enabling contact-rich tasks that link low-dimensional state generation with fine post-contact modulation.

What carries the argument

Two linked forms of structural intelligence: structural prior generation via wrist-to-finger tenodesis, FDS/FDP routing and the dorsal extensor hood, plus muscle-mediated modulation via extrinsic muscles, lumbricals and interossei that regulate MCP posture, distal stability and fingertip force paths.

If this is right

  • Wrist posture alone induces multi-joint pre-shaping through tenodesis and routing without extra actuators.
  • The extensor hood produces coupled PIP-to-DIP coordination from a single posture input.
  • Intrinsic muscle pathways allow post-grasp adjustment of distal stability and fingertip force direction.
  • Contact-rich tasks become feasible when low-dimensional state generation is combined with mechanical modulation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same structural-prior principle could be applied to other limbs to lower overall actuator and computation demands.
  • Designs that vary ligament stiffness or tendon paths could reveal how much passive behavior is optimal versus how much must remain active.
  • Integration with full-arm or torso mechanisms might extend the same low-dimensional control advantage to whole-body tasks.
  • Testing the hand under reduced sensor feedback would show how far the mechanical priors can compensate for missing information.

Load-bearing premise

The observed task performance arises primarily from the copied anatomical structures rather than from unmentioned control algorithms, sensor feedback, or the specific fabrication tolerances of the prototype.

What would settle it

Construct an otherwise identical hand that lacks the anatomical tendon routings, ligament constraints and extensor hood, then test whether it can match the MCR-Bionic performance on the same coin-rotation and cube-manipulation tasks under identical low-dimensional control inputs.

Figures

Figures reproduced from arXiv: 2606.13601 by Guowu Wei, Haosen Yang.

Figure 1
Figure 1. Figure 1: Implementation framework of anatomical pathways in MCR-Bionic. (A) The 1:1 musculoskeletal biomimetic hand platform, including the closed [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Functional demonstrations and geometric mechanical analysis of MCR-Bionic. (A) Functional chain from wrist input to default grasping, muscle [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Anatomical reconstruction of MCR-Bionic. (A–D) Wrist skeletal and ligament system and corresponding anterior/posterior prototype views. (E,F) [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Working mechanism of the extensor hood. (a,b) Prototype views showing the central slip, lateral bands, and PIP/DIP flexion. (c–j) Schematics of [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Joint mechanics model of the interosseous, lumbrical, and extensor [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Tenodesis coupling by which wrist extension induces passive finger [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
read the original abstract

Dexterous robotic hands are usually formulated as high dimensional active control systems governed by degrees of freedom, actuation, and algorithms. Human hand dexterity, however, is partly encoded in the physical architecture of bones, ligaments, tendons, aponeuroses, and intrinsic muscles. This work describes that contribution as two linked forms of structural intelligence: structural prior generation, in which wrist to finger tenodesis, FDS/FDP routing, and the dorsal extensor hood transform low dimensional posture inputs into default grasp configurations and PIP to DIP coordination; and muscle mediated modulation, in which extrinsic muscles, lumbricals, and interossei regulate MCP posture, distal stability, fingertip force paths, and contact states around that default state. Based on this framework, MCR-Bionic Hand is developed as a 1:1 musculoskeletal biomimetic hand integrating a two row eight bone wrist, cross wrist tendons, anatomical flexor routing, volar plate and collateral ligament constraints, the dorsal extensor hood, and intrinsic muscle pathways within one body. Functional demonstrations and geometric mechanical models show that wrist posture induces multi joint pre shaping, the extensor hood maps PIP posture to a coupled DIP response, and intrinsic plus pathways modulate distal stability and fingertip action direction after grasp formation. Contact rich tasks, including coin rotation, pen transfer, dorsal coin flipping, and cube manipulation, show that MCR-Bionic links low dimensional state generation with fine post contact modulation. These results suggest that anatomical biomimetics is valuable not for visual similarity, but for identifying human hand structures that perform part of control.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper claims that human hand dexterity is partly encoded in physical anatomical structures (bones, ligaments, tendons, extensor hood, intrinsic muscles) rather than solely in high-dimensional control algorithms. It introduces the MCR-Bionic Hand as a 1:1 musculoskeletal biomimetic prototype incorporating a two-row wrist, anatomical flexor routing, volar plates, collateral ligaments, dorsal extensor hood, and intrinsic pathways. These are said to enable 'structural prior generation' (wrist-to-finger tenodesis and hood transforming low-dim inputs into grasp pre-shapes and PIP-DIP coordination) and 'muscle-mediated modulation' (regulating MCP posture, distal stability, and fingertip forces). Geometric models and functional demonstrations on tasks such as coin rotation, pen transfer, dorsal coin flipping, and cube manipulation are presented to support that the structures perform part of the control.

Significance. If the central attribution holds, the work would provide concrete evidence that specific anatomical structures can reduce the dimensionality of active control by encoding default behaviors and post-contact modulation, shifting emphasis from purely algorithmic approaches to hybrid structural intelligence in dexterous robotics.

major comments (2)
  1. [Abstract] Abstract: the claim that the listed structures 'perform part of control' (transforming low-dim inputs into pre-shapes and modulating post-contact forces) is load-bearing but unsupported by any ablation, quantitative metrics, or comparison; only functional demonstrations and geometric models are described, with no error bars, success rates, or baseline non-biomimetic hand under identical low-dim actuation.
  2. [Abstract] Abstract: no description of the actuation scheme, sensor feedback, or control policy used during the contact-rich tasks is provided, preventing isolation of the structural priors' contribution from possible implicit high-dimensional control or fabrication effects.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important aspects of evidence presentation and experimental description that we will address. Below we respond point-by-point to the major comments.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that the listed structures 'perform part of control' (transforming low-dim inputs into pre-shapes and modulating post-contact forces) is load-bearing but unsupported by any ablation, quantitative metrics, or comparison; only functional demonstrations and geometric models are described, with no error bars, success rates, or baseline non-biomimetic hand under identical low-dim actuation.

    Authors: The geometric mechanical models in the manuscript provide explicit mechanistic quantification of how wrist-to-finger tenodesis, flexor routing, and the extensor hood map low-dimensional wrist inputs to multi-joint pre-shapes and PIP-DIP coordination, while intrinsic pathways alter post-contact force directions. The contact-rich task demonstrations illustrate that these behaviors emerge under low-dimensional tendon actuation without additional high-dimensional policies. We acknowledge that the absence of ablations against non-biomimetic baselines and statistical metrics (error bars, success rates) leaves the attribution partly qualitative. In revision we will add any available quantitative task metrics and explicitly discuss the limitations of the current evidence base. revision: partial

  2. Referee: [Abstract] Abstract: no description of the actuation scheme, sensor feedback, or control policy used during the contact-rich tasks is provided, preventing isolation of the structural priors' contribution from possible implicit high-dimensional control or fabrication effects.

    Authors: The full manuscript specifies tendon-driven actuation via motors pulling the extrinsic and intrinsic pathways, with low-dimensional open-loop position commands applied to wrist posture and muscle tensions. No closed-loop sensor feedback or high-dimensional controllers are employed; task performance relies on visual observation only. We will expand the methods and experimental sections to explicitly document the actuation hardware, command dimensionality, absence of feedback loops, and control policy so that the structural contribution can be more clearly isolated from fabrication or implicit control effects. revision: yes

Circularity Check

0 steps flagged

No circularity; physical design without mathematical reduction to inputs

full rationale

The paper presents a physical biomimetic robotic hand design copying human anatomical structures (wrist bones, tendons, extensor hood, intrinsics) and demonstrates task performance (coin rotation, pen transfer, etc.) via geometric models and prototypes. No equations, fitted parameters, predictions, or derivation chains appear in the provided text. Claims rest on physical implementation and empirical results rather than any self-referential definitions, fitted inputs renamed as predictions, or self-citation load-bearing steps. The central attribution of dexterity to structural priors is an empirical design claim, not a mathematical reduction that collapses to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the design implicitly assumes anatomical structures transfer directly to mechanical performance without additional validation.

pith-pipeline@v0.9.1-grok · 5819 in / 990 out tokens · 14814 ms · 2026-06-27T06:23:48.211863+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

54 extracted references · 7 canonical work pages

  1. [1]

    F. J. Valero-Cuevas, ”Predictive modulation of muscle coordination pattern magnitude scales fingertip force magnitude over the voluntary range,” Journal of Neurophysiology, vol. 83, no. 3, pp. 1469–1479, 2000

    2000

  2. [2]

    Garcia-Elias, K.-N

    M. Garcia-Elias, K.-N. An, L. Berglund, R. L. Linscheid, W. P. Cooney III, and E. Y . Chao, ”Extensor mechanism of the fingers. i. a quantitative geometric study,” The Journal of hand surgery, vol. 16, no. 6, pp. 1130–1136, 1991

    1991

  3. [3]

    J. N. Wilson, ”Providing automatic grasp by flexor tenodesis,” JBJS, vol. 38, no. 5, pp. 1019–1024, 1956

    1956

  4. [4]

    K. N. Shah, J. D. Hodax, and J. A. Katarincic, ”Evaluating a flexor tendon laceration with the tenodesis effect,” The Journal of Pediatrics, vol. 176, pp. 214–214, 2016

    2016

  5. [5]

    Landsmeer and C

    J. Landsmeer and C. Long, ”The mechanism of finger control, based on electromyograms and location analysis,” Cells Tissues Organs, vol. 60, no. 3, pp. 330–347, 1965

    1965

  6. [6]

    S. M. Craig, ”Anatomy of the joints of the fingers,” Hand clinics, vol. 8, no. 4, pp. 693–700, 1992

    1992

  7. [7]

    R. W. Haines, ”The extensor apparatus of the finger,” Journal of anatomy, vol. 85, no. Pt 3, p. 251, 1951

    1951

  8. [8]

    J. A. Clavero, P. Golan ´o, O. Farinas, X. Alomar, J. M. Monill, and M. Esplugas, ”Extensor mechanism of the fingers: Mr imaging–anatomic correlation,” Radiographics, vol. 23, no. 3, pp. 593–611, 2003

    2003

  9. [9]

    Z. Li, V . Zatsiorsky, and M. Latash, ”Contribution of the extrinsic and intrinsic hand muscles to the moments in finger joints,” Clinical biomechanics, vol. 15, no. 3, pp. 203–211, 2000

    2000

  10. [10]

    M. K. Goin, ”Structural and dynamic basis of hand surgery,” Plastic and Reconstructive Surgery, vol. 65, no. 6, pp. 846–847, 1980

    1980

  11. [11]

    Hamada, K

    Y . Hamada, K. Sairyo, N. Hibino, and A. Kobayashi, ”Correction of severe contracture of intrinsic plus hand with a modified ilizarov minifixator,” Journal of Hand and Microsurgery, vol. 7, no. 01, pp. 161–165, 2015

    2015

  12. [12]

    Huang, D

    Y . Huang, D. Fan, H. Duan, D. Yan, W. Qi, J. Sun, Q. Liu, and P. Wang, ”Human-like dexterous manipulation for anthropomorphic five-fingered hands: A review,” Biomimetic Intelligence and Robotics, p. 100212, 2025

    2025

  13. [13]

    Zhang, P

    N. Zhang, P. Zhou, X. Yang, F. Shen, J. Ren, T. Hou, L. Dong, R. Bian, D. Wang, G. Gu, et al., ”Biomimetic rigid-soft finger design for highly dexterous and adaptive robotic hands,” Science Advances, vol. 11, no. 17, p. eadu2018, 2025

    2025

  14. [14]

    Zhang, J

    N. Zhang, J. Ren, Y . Dong, X. Yang, R. Bian, J. Li, G. Gu, and X. Zhu, ”Soft robotic hand with tactile palm-finger coordination,” Nature Communications, vol. 16, no. 1, p. 2395, 2025

    2025

  15. [15]

    Z. Zhao, W. Li, Y . Li, T. Liu, B. Li, M. Wang, K. Du, H. Liu, Y . Zhu, Q. Wang, et al., ”Embedding high-resolution touch across robotic hands enables adaptive human-like grasping,” Nature Machine Intelligence, pp. 1–12, 2025

    2025

  16. [16]

    Sankar, W.-Y

    S. Sankar, W.-Y . Cheng, J. Zhang, A. Slepyan, M. M. Iskarous, R. J. Greene, R. DeBrabander, J. Chen, A. Gupta, and N. V . Thakor, ”A natural biomimetic prosthetic hand with neuromorphic tactile sensing for precise and compliant grasping,” Science Advances, vol. 11, no. 10, p. eadr9300, 2025

    2025

  17. [17]

    J. Luo, C. Xu, J. Wu, and S. Levine, ”Precise and dexterous robotic manipulation via human-in-the-loop reinforcement learning,” Science Robotics, vol. 10, no. 105, p. eads5033, 2025

    2025

  18. [18]

    Kaneko, K

    K. Kaneko, K. Harada, and F. Kanehiro, ”Development of multi-fingered hand for life-size humanoid robots,” Journal of the Robotics Society of Japan, vol. 26, no. 1, pp. 98–109, 2008

    2008

  19. [19]

    Takaki and T

    T. Takaki and T. Omata, ”High-performance anthropomorphic robot hand with grasping-force-magnification mechanism,” IEEE/ASME Transactions on mechatronics, vol. 16, no. 3, pp. 583–591, 2010

    2010

  20. [20]

    Junge and J

    K. Junge and J. Hughes, ”Adapt-teleop: robotic hand with human matched embodiment enables dexterous teleoperated manipulation,” npj Robotics, vol. 3, no. 1, p. 31, 2025

    2025

  21. [21]

    Singh, M

    A. Singh, M. Pinto, P. Kaltsas, S. Pirozzi, S. Sulaiman, and F. Ficuciello, ”Validations of various in-hand object manipulation strategies employing a novel tactile sensor developed for an under-actuated robot hand,” Frontiers in Robotics and AI, vol. 11, p. 1460589,2024

    2024

  22. [22]

    M. G. Catalano, G. Grioli, E. Farnioli, A. Serio, C. Piazza, and A. Bicchi, ”Adaptive synergies for the design and control of the pisa/iit softhand,” The International Journal of Robotics Research, vol. 33, no. 5, pp. 768– 782, 2014

    2014

  23. [23]

    Y . Jin, Y . Zheng, Y . Mao, Z. Li, L. Pan, B. Yang, S. Cai, S. Hussain, and G. Bao, ”An underactuated humanoid multi-finger dexterous hand with adaptive synergic grasping,” in International Conference on Intelligent Robotics and Applications. Springer, 2024, pp. 348–365

    2024

  24. [24]

    AboZaid, M

    Y . AboZaid, M. T. Aboelrayat, I. S. Fahim, and A. G. Radwan, ”Soft robotic grippers: A review on technologies, materials, and applications,” Sensors and Actuators A: Physical, vol. 372, p. 115380, 2024

    2024

  25. [25]

    H. Hua, X. Wu, C. Zhao, Z. Wu, J. Song, and Z. Liao, ”Design and evaluation of a pneumatic underactuated robotic gripper with in-hand multi-mode self-perception ability,” Journal of Mechanical Science and Technology, pp. 1–11, 2025

    2025

  26. [26]

    Ou Yang, S.-Y

    C.-W. Ou Yang, S.-Y . Yu, C.-W. Chan, C.-Y . Tseng, J.-F. Cai, H.- P. Huang, and J.-Y . Juang, ”Enhancing the versatility and perfor- mance of soft robotic grippers, hands, and crawling robots through threedimensional-printed multifunctional buckling joints,” Soft Robotics, vol. 11, no. 5, pp. 741–754, 2024

    2024

  27. [27]

    Malas, G

    D. Malas, G. Zhang, S. Wang, W. Huang, L. Lindenroth, B. Yang, W. Xia, and H. Liu, ”Fluidic multichambered actuator and multiaxis intrinsic force sensing,” Soft Robotics, vol. 12, no. 1, pp. 81–94, 2025

    2025

  28. [28]

    Riener, L

    R. Riener, L. Rabezzana, and Y . Zimmermann, ”Do robots outperform humans in human-centered domains?” Frontiers in Robotics and AI, vol. 10, p. 1223946, 2023

    2023

  29. [29]

    X. Wang, R. Wei, Z. Chen, H. Pang, H. Li, Y . Yang, Q. Hua, and G. Shen, ”Bioinspired intelligent soft robotics: From multidisci- plinary in- tegration to next-generation intelligence,” Advanced Science, p. e06296, 2025

    2025

  30. [30]

    Y . Wang, T. Hao, Y . Liu, H. Xiao, S. Liu, and H. Zhu, ”Anthropomorphic soft hand: Dexterity, sensing, and machine learning,” in Actuators, vol. 13, no. 3. MDPI, 2024, p. 84

    2024

  31. [31]

    Suthar, M

    B. Suthar, M. I. Awad, L. Seneviratne, Y . Zweiri, and I. Hussain, ”Design of robotic finger using twisted string actuator with modular passive return rotational joints to achieve high grasping force: Application to wearable sixth finger,” Mechatronics, vol. 99, p. 103157, 2024

    2024

  32. [32]

    J. T. Belter, J. L. Segil, et al., ”Mechanical design and performance specifications of anthropomorphic prosthetic hands: A review,” Journal of rehabilitation research and development, vol. 50, no. 5, p. 599, 2013

    2013

  33. [33]

    Gopura, D

    R. Gopura, D. Bandara, N. Gunasekera, V . Hapuarachchi, and B. Ariyarathna, ”A prosthetic hand with self-adaptive fingers,” in 2017 3rd international conference on control, automation and robotics (ICCAR). IEEE, 2017, pp. 269–274

    2017

  34. [34]

    L. U. Odhner, L. P. Jentoft, M. R. Claffee, N. Corson, Y . Tenzer, R. R. Ma, M. Buehler, R. Kohout, R. D. Howe, and A. M. Dollar, ”A com- pliant, underactuated hand for robust manipulation,” The International Journal of Robotics Research, vol. 33, no. 5, pp. 736–752, 2014

    2014

  35. [35]

    Deimel and O

    R. Deimel and O. Brock, ”A novel type of compliant and underactuated robotic hand for dexterous grasping,” The International Journal of Robotics Research, vol. 35, no. 1-3, pp. 161–185, 2016

    2016

  36. [36]

    Santos, G

    B. Santos, G. Gonc ¸alves, and V . H. Pinto, ”Shadow dexterous hand: Kinematic retargeting algorithms comparison,” in International Confer- ence on Optimization, Learning Algorithms and Applications. Springer, 2025, pp. 144–156

    2025

  37. [37]

    Grebenstein, M

    M. Grebenstein, M. Chalon, W. Friedl, S. Haddadin, T. Wimb ¨ock, G. Hirzinger, and R. Siegwart, ”The hand of the dlr hand arm system: De- signed for interaction,” The International Journal of Robotics Research, vol. 31, no. 13, pp. 1531–1555, 2012. 16

    2012

  38. [38]

    Xu and E

    Z. Xu and E. Todorov, ”Design of a highly biomimetic anthropo- morphic robotic hand towards artificial limb regeneration,” in Pro- ceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 2016, pp. 3485–3492, doi: 10.1109/ICRA.2016.7487528

    work page doi:10.1109/icra.2016.7487528 2016

  39. [39]

    Ozawa, K

    R. Ozawa, K. Hashirii, Y . Yoshimura, M. Moriya, and H. Kobayashi, ”Design and control of a three-fingered tendon-driven robotic hand with active and passive tendons,” Autonomous Robots, vol. 36, no. 1, pp. 67–78, 2014

    2014

  40. [40]

    Y . Zhu, G. Wei, L. Ren, Z. Luo, and J. Shang, ”An anthropomorphic robotic finger with innate human-finger-like biomechanical advantages Part I: Design, ligamentous joint, and extensor mechanism,” IEEE Transactions on Robotics, vol. 39, no. 1, pp. 485–504, 2023, doi: 10.1109/TRO.2022.3200006

    work page doi:10.1109/tro.2022.3200006 2023

  41. [41]

    S. H. Sadati, M. ElDiwiny, S. Nurzaman, F. Iida, and T. Nanayakkara, ”Embodied intelligence & morphological computation in soft robotics community: Collaborations, coordination, and perspective,” in IOP Con- ference Series: Materials Science and Engineering, vol. 1261, no. 1. IOP Publishing, 2022, p. 012005

    2022

  42. [42]

    D. Liu, L. He, D. Yang, J. Zhang, J. Chen, and G. Yang, ”Advances in digital twins technology of human skeletal muscle,” Sheng wu yi xue Gong Cheng xue za zhi= Journal of Biomedical Engineering= Shengwu Yixue Gongchengxue Zazhi, vol. 40, no. 4, pp. 784–791, 2023

    2023

  43. [43]

    T. Sun, X. He, X. Song, L. Shu, and Z. Li, ”The digital twin in medicine: a key to the future of healthcare?” Frontiers in Medicine, vol. 9, p. 907066, 2022

    2022

  44. [44]

    O. I. Franko, T. M. Winters, T. F. Tirrell, E. R. Hentzen, and R. L. Lieber, ”Moment arms of the human digital flexors,” Journal of biomechanics, vol. 44, no. 10, pp. 1987–1990, 2011

    1987

  45. [45]

    Yang, S.-C

    T.-H. Yang, S.-C. Lu, W.-J. Lin, K. Zhao, C. Zhao, K.-N. An, I.M. Jou, P.-Y . Lee, L.-C. Kuo, and F.-C. Su, ”Assessing finger joint biomechanics by applying equal force to flexor tendons in vitro using a novel simultaneous approach,” PloS one, vol. 11, no. 8, p. e0160301, 2016

    2016

  46. [46]

    A. M. Kociolek and P. J. Keir, ”Modelling tendon excursions and moment arms of the finger flexors: anatomic fidelity versus function,” Journal of biomechanics, vol. 44, no. 10, pp. 1967–1973, 2011

    1967

  47. [47]

    Shadow Dexterous Hand Technical Specification,

    Shadow Robot Company, “Shadow Dexterous Hand Technical Specification,” Dec. 2024. [Online]. Available: https://shadowrobot. com/wp-content/uploads/2024/12/shadow dexterous hand e technical specification 20241204.pdf. Accessed: Jun. 7, 2026

    2024

  48. [48]

    U. Kim, D. Jung, H. Jeong, J. Park, H.-M. Jung, J. Cheong, H. R. Choi, H. Do, and C. Park, ”Integrated linkage-driven dexterous anthropomorphic robotic hand,” Nature Communications, vol. 12, no. 1, p. 7177, 2021, doi: 10.1038/s41467-021-27261-0

    work page doi:10.1038/s41467-021-27261-0 2021

  49. [49]

    Avail- able: https://allonic.co/

    Allonic Inc., ”Allonic robotics and 3D tissue braiding.” [Online]. Avail- able: https://allonic.co/. Accessed: Jun. 7, 2026

    2026

  50. [50]

    Available: https: //clonerobotics.com/hand/

    Clone Robotics, ”Clone Hand.” [Online]. Available: https: //clonerobotics.com/hand/. Accessed: Jun. 7, 2026

    2026

  51. [51]

    A. D. Deshpande, Z. Xu, M. J. Vande Weghe, B. H. Brown, J. Ko, L. Y . Chang, D. D. Wilkinson, S. M. Bidic, and Y . Matsuoka, ”Mechanisms of the anatomically correct testbed hand,” IEEE/ASME Transactions on Mechatronics, vol. 18, no. 1, pp. 238–250, 2013, doi: 10.1109/TMECH.2011.2166801

    work page doi:10.1109/tmech.2011.2166801 2013

  52. [52]

    B. J. Tasi, M. Koller, and G. Cserey, ”Design of the anatomically correct, biomechatronic hand,” arXiv preprint arXiv:1909.07966, 2019, doi: 10.48550/arXiv.1909.07966

    work page doi:10.48550/arxiv.1909.07966 1909

  53. [53]

    L. Tian, J. Zheng, N. Magnenat Thalmann, H. Li, Q. Wang, J. Tao, and Y . Cai, ”Design of a single-material complex structure anthropomorphic robotic hand,” Micromachines, vol. 12, no. 9, p. 1124, 2021, doi: 10.3390/mi12091124

    work page doi:10.3390/mi12091124 2021

  54. [54]

    H. Yang, G. Wei, L. Ren, Z. Qian, K. Wang, H. Xiu, and W. Liang, ”A low-cost linkage-spring-tendon-integrated compliant anthropomorphic robotic hand: MCR-Hand III,” Mechanism and Machine Theory, vol. 158, p. 104210, 2021, doi: 10.1016/j.mechmachtheory.2020.104210

    work page doi:10.1016/j.mechmachtheory.2020.104210 2021