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REVIEW 4 major objections 4 minor 28 references

A Dual-Bearing Magnetorheological Grease Clutch with Intention-Based Demagnetization for Wearable Haptic Feedback

T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A 450 g magnetorheological clutch delivers 42 N·m and graded force feedback for teleoperation.

desk verdict The abstract advertises a demagnetization and user-study paper; the full text is a different teleoperation exoskeleton paper, so the submission as-is is not reviewable. read the letter →

arxiv 2506.15124 v2 pith:Y4B5BBIP submitted 2025-06-18 eess.SY cs.SY

Zhongyuan Kong , Lei Li , Erwin Ang Tien Yew , Zirui Chen , Wenbo Li , Shiwu Zhang , Jian Yang , Shuaishuai Sun This is my paper · ORCID
classification eess.SYcs.SY
keywords magnetorheologicalclutchMRgreaseupper-limbexoskeletonteleoperationforcefeedbacktorque-to-massratiosurfaceelectromyographysemi-activeactuator
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The submitted full text argues that a semi-active upper-limb exoskeleton built around magnetorheological clutches can deliver safe, lightweight force feedback for teleoperation. It reports a dual-bearing MR grease clutch that weighs 450 g, reaches a locking torque of 42.12 N·m at 1.3 A, and achieves a torque-to-mass ratio of 93.6 N·m/kg, about 246% higher than a reference MR clutch design. The paper validates the concept through master–slave trajectory tracking, collision detection, and stiffness recognition experiments in which the operator's muscle activation tracks the rendered contact force. The abstract of the submission, however, describes a different study with different numbers and experiments, so this pith follows the full text.

What carries the argument

The central object is the dual-bearing MR clutch, in which two deep-groove ball bearings filled with magnetorheological grease are placed on either side of an excitation coil so that the coil's magnetic field passes through both bearing gaps. The torque–current relationship is captured by the Hill function $f(x)=V_{max}x^n/(K^n+x^n)$ with fitted parameters $V_{max}=54.28$, $K=0.66$, $n=1.96$, giving the controller a compact map from current to locking torque. A decaying-amplitude sinusoidal demagnetizing current is also applied after excitation to reduce the sticky release caused by magnetic hysteresis.

What would settle it

Run the reported torque–current test on a dynamometer with a 450 g dual-bearing MR grease clutch at 1.3 A: if the torque does not approach 42 N·m and follow the Hill curve with $V_{max}=54.28$, $K=0.66$, $n=1.96$, the full-text performance claim fails; separately, the abstract's demagnetization-release and transparency claims would require a controlled release-time and user-study experiment, which the full text does not include.

Watch

Extended reading notes

Core claim

The central claim is that a dual-bearing structure consisting of two grease-packed ball bearings flanking a coil creates a magnetic circuit that saturates at 1.3 A and locks the rotor with 42.12 N·m of torque, while still allowing near-free rotation at zero current. Because the clutch is semi-active, it can only dissipate energy, so it cannot push the operator's joints; this makes it intrinsically safe for wearable use. The authors further show that scaling the commanded torque up to five times the measured slave force makes small contact forces perceptible, and that the rendered torque produces muscle-activation responses consistent with the expected effort for objects of different stiffness.

Load-bearing premise

The load-bearing premise is that the full text attached is the paper the abstract describes; the two parts report different torque values (42.12 vs 43.42 N·m), different torque-to-mass ratios (93.6 vs 96.5 N·m/kg), and different experiments (teleoperation with muscle signals versus demagnetization release with user studies).

Editorial extensions

If this is right

  • A 450 g MR clutch can replace heavier motor-and-gear actuators in exoskeletons, improving wearability while keeping the joint backdrivable at zero current.
  • The Hill-function torque map lets the controller command a desired locking torque directly from the measured slave-side force, with no need for online torque feedback.
  • Because the actuator is semi-active, a power failure releases the joint instead of locking it, a useful safety property for human-robot interaction.
  • The demonstrated sEMG correspondence suggests the clutch can render not just collision onset but graded stiffness, which is what multi-level kinesthetic feedback requires.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The abstract promises an intention-based demagnetization release control and user studies on release transparency; the full text does not report such experiments, so those specific claims remain unverified by this document.
  • The dual-bearing MR grease design avoids the sealing problems of MR fluid devices; a natural next test is repeated cycling to see whether grease migration or particle settling degrades the 42 N·m torque over hundreds of cycles.
  • The muscle-activation validation could be complemented by psychophysical tests, such as just-noticeable differences in rendered stiffness, to quantify perceived transparency rather than muscle effort.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The manuscript under review, arXiv:2506.15124, is presented as a paper on a dual-bearing magnetorheological grease clutch with intention-based demagnetization for wearable haptic feedback. The abstract advertises a maximum locking torque of 43.42 N·m at 1.3 A, a torque-to-mass ratio of 96.5 N·m/kg, and a control strategy that accelerates clutch release and improves perceived release transparency and contact-to-release smoothness, validated by bench tests, replay validation, teleoperation experiments, and user studies. The actual full text describes an upper-limb exoskeleton for teleoperation using MR clutches, with a maximum torque of 42.12 N·m and a torque-to-mass ratio of 93.6 N·m/kg (Table 1 and Fig. 2(f)). The body contains a Hill-function fit (Eq. 1) of torque versus current, a description of a demagnetizing current waveform and a manual demagnetization button (Sec. II.C.1), and sEMG-based validation of force feedback in obstacle avoidance and stiffness recognition tasks (Sec. III.B). The abstract's claimed demagnetization-driven release experiments, user studies, and release-transparency metrics are not present in the body. The body's claims are thus narrower and different from the abstract's headline claims.

Significance. If the body's claims are taken on their own terms, the prototype demonstrates a competitive torque-to-mass ratio (93.6 N·m/kg, Table 1) and a lightweight semi-active force-feedback exoskeleton, which would be a useful engineering contribution. However, the abstract's headline claims of intention-based demagnetization and improved release transparency are exactly the claims that would make this paper distinctive for wearable haptics, and they are unsupported by the submitted full text. The torque model in Eq. (1) is a fitted Hill curve, not a physics-inspired derivation, and the sEMG validation is a self-consistency check against the same calibration curve. Neither provides independent evidence of haptic benefit. The paper's significance is therefore substantially reduced to a hardware demonstration with limited validation.

major comments (4)
  1. [Abstract vs. Full Text] The abstract's central quantitative claims conflict with the body: the abstract reports a maximum locking torque of 43.42 N·m at 1.3 A and a torque-to-mass ratio of 96.5 N·m/kg, whereas Sec. II.B.2 and Table 1 report 42.12 N·m and 93.6 N·m/kg. The abstract also claims user studies and replay validation supporting improved release transparency and contact-to-release smoothness, but the body contains no user study, no release-time measurement, no comparison of demagnetized versus simple current cutoff, and no perceptual metrics. As submitted, the abstract's headline result is unverifiable from the body.
  2. [Sec. II.B.2, Eq. (1)] The 'physics-inspired interpretive model' is presented in Eq. (1) as a Hill function f(x)=Vmax*x^n/(K^n+x^n) with fitted parameters Vmax=54.28, K=0.66, and n=1.96. No derivation from magnetic circuit physics or rheological model is given, and the parameters are free parameters fitted to the measured torque-current data. The model is therefore a curve fit used as the control mapping, not an independent physics-based derivation as claimed in the abstract.
  3. [Sec. III.B] The sEMG validation is circular in the sense that the same calibration curve established in Sec. III.B (Fig. 5(a)) is used to judge whether measured sEMG values are 'expected.' For example, the text notes that observed RMS sEMG values of 138 µV and 129 µV differ from the calibration predictions of 141 µV and 118 µV but are treated as acceptable within a 20 µV margin. This does not independently verify force feedback fidelity; it only checks consistency with the calibration procedure. No comparison with a no-feedback or alternative-feedback condition is provided.
  4. [Sec. II.C.1] The demagnetization control is described qualitatively as a decaying sinusoidal current with a manual demagnetization button, but no experiments measure whether this accelerates clutch release relative to simply cutting the current. Without release-time data or a comparison condition, the abstract's claim that intention-based demagnetization 'accelerates clutch release and improves perceived release transparency' is unsupported by the body.
minor comments (4)
  1. [Sec. II.C.1] The text says 'When the excitation current applied to the MR clutch reaches 1.3 A, the driver board imposes a threshold limit to prevent further increase in current,' but the current-limit rationale and implications for torque saturation are not discussed in the control design.
  2. [Sec. II.B.2] The sentence 'The experimental data are fitted using the Hill function.The data in Fig. 2(f) are fitted using the Hill function.' is duplicated; please remove the redundant phrase.
  3. [Sec. III.B.1] The phrase 'an root mean square (RMS) torque' should be 'a root mean square (RMS) torque'.
  4. [Sec. IV] The conclusion restates 'a maximum locking torque of 42 N·m' without specifying the current, while the body states 42.12 N·m at 1.3 A; please ensure consistency across the paper.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation found; the main concern is an abstract/full-text mismatch, which is a support/consistency issue rather than a circularity defect.

full rationale

The body's central hardware claims are externally grounded: Section II.B.2 reports directly measured locking torque (42.12 N·m at 1.3 A from Fig. 2(f)) and computes TMR, TVR, and TPR from measured mass, volume, and power in Table I. Equation (1) is explicitly a Hill-function fit to the measured torque-current data with reported MAE/RMSE/nRMSE, and using that fitted mapping to select coil currents in the teleoperation experiments is standard empirical modeling, not a prediction equivalent to its inputs. The sEMG calibration in Section III.B builds a reference curve and then compares measured sEMG to expected values read from that curve; this is a calibration/consistency check rather than an independent derivation, but it is not presented as a first-principles prediction and no load-bearing hardware claim reduces to it. There is no self-citation chain, no imported uniqueness theorem, and the comparative claims in Table I are against external prior designs [1],[23],[24]. The genuine problem with this submission is not circularity: the attached full text ('A Force Feedback Exoskeleton for Teleoperation Using Magnetorheological Clutches') does not contain the abstract's advertised intention-based demagnetization control, release-time experiments, or user studies, and the headline torque numbers differ (43.42 vs 42.12 N·m). That is a completeness/consistency failure, which is outside the circularity definition and therefore does not raise the circularity score.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central torque model is a Hill function with three fitted parameters, and the sEMG validation relies on a calibration curve plus an ad hoc tolerance. The COMSOL simulation is accepted as ground truth for magnetic saturation. No first-principles derivation links current to torque beyond the magnetic circuit statement. The abstract's demagnetization control is absent from the body, so no ledger entries for it can be extracted.

free parameters (5)
  • Vmax (Hill function) = 54.28 N·m
    Fitted to the torque-current data in Fig. 2(f) via Eq. (1).
  • K (Hill function) = 0.66 A
    Fitted to the torque-current data in Fig. 2(f).
  • n (Hill function) = 1.96
    Fitted to the torque-current data in Fig. 2(f).
  • Force feedback gain = 5x
    Chosen by hand in Section III.B to amplify the slave-side force by a factor of five.
  • sEMG normal fluctuation margin = 20 µV
    Ad hoc threshold stated in Section III.B.1 to justify agreement between measured and expected sEMG values.
assumptions (4)
  • domain assumption The MRG torque-current relationship can be modeled by a Hill function (Eq. 1).
    The text fits the measured data with a Hill function but provides no physical derivation, calling it a physics-inspired interpretive model in the abstract.
  • domain assumption COMSOL magnetic field simulation accurately represents the physical device.
    The simulation is used to conclude that the magnetic field saturates near 1.3 A, with only three probe measurements as validation.
  • domain assumption Biceps sEMG is a valid proxy for perceived force feedback torque.
    A calibration curve between sEMG and torque is used to convert measured sEMG into an expectation of perceived torque in Section III.B.
  • standard math The exoskeleton can be modeled as a five-DOF four-bar linkage with a virtual fifth joint.
    The D-H kinematic model in Section II.C.2 is standard, but the virtual fifth joint assumption affects the mapping accuracy.

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Cite this review

Pith. "Pith review of A Dual-Bearing Magnetorheological Grease Clutch with Intention-Based Demagnetization for Wearable Haptic Feedback." pith.science (2026). https://pith.science/paper/Y4B5BBIP

@misc{pith2026250615124,
  author       = {Pith},
  title        = {Pith review of: A Dual-Bearing Magnetorheological Grease Clutch with Intention-Based Demagnetization for Wearable Haptic Feedback},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Y4B5BBIP}},
  note         = {Machine review of arXiv:2506.15124}
}
abstract

This paper presents the design, modeling, and control of a dual-bearing magnetorheological grease (MRG) clutch for wearable haptic feedback. Compared with conventional MR fluid devices, the proposed clutch avoids leakage-related reliability degradation while achieving high torque density in a compact structure. To provide physical insight into the torque-generation mechanism, a physics-inspired interpretive model is introduced to capture the dominant relationship among excitation current, magnetic-field evolution in the bearing gaps, and clutch locking torque. To mitigate the undesirable ``sticky'' sensation caused by passive bidirectional braking, an intention-based control strategy with active demagnetization is further developed to enable smoother release during human--robot interaction. Experimental characterization shows that the proposed clutch achieves a maximum locking torque of 43.42\,N$\cdot$m at 1.3\,A and a torque-to-mass ratio of 96.5\,N$\cdot$m/kg. Bench tests, replay validation, teleoperation experiments, and user studies indicate that the proposed approach accelerates clutch release and improves perceived release transparency and contact-to-release smoothness, while maintaining effective multi-level kinesthetic rendering.

Figures

Figures reproduced from arXiv: 2506.15124 by the authors.

Figure 1
Figure 1. Design of teleoperation and force feedback workflow. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Structure, working principle, and magnetic field analysis of the MR clutch. (a) Cutaway view of MR clutch structure. (b) Principle of MR bearings. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Force Sensor Data Driver circuit Control signal VINA … VNH7040AY Power management Coil 1 Coil 4 … Voltages Voltages Control USART Data analysis Drive for MR damper MCU(STM32F103) Functional module VPWM VINB OutA OutB … VINA VNH7040AY VPWM VINB OutA OutB Channel1 Channel4 data mapping 6S LiPo battery 3V3 20V 20V [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: (a) Single-arm upper limb exoskeleton based on MR clutches. (b) D-H [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Teleoperated exoskeleton for simulated lunar surface sampling tasks. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Comparative analysis of torque and sEMG signals during collisions [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

28 extracted references · 27 canonical work pages

  1. [1]

    High-performance magneto-rheological clutches for direct-drive actuation: Design and development,

    S. Pisetskiy and M. Kermani, “High-performance magneto-rheological clutches for direct-drive actuation: Design and development,” Journal of Intelligent Material Systems and Structures , vol. 32, no. 20, pp. 2582– 2600, Oct. 2021

    work page 2021

  2. [2]

    Design and verification of manned space station teleoperation rendezvous and docking system,

    Z. Guo, J. An, G. Fan, L. Ying, X. Zhang, and T. Li, “Design and verification of manned space station teleoperation rendezvous and docking system,” in Proc. IEEE Int. Conf. Cybern. Intell. Syst. (CIS) and IEEE Int. Conf. Robot., Autom. Mechatronics (RAM) , 2024, pp. 561–566

    work page 2024

  3. [3]

    Unified contact model and hybrid motion/force control for teleoperated manipulation in unknown environments,

    F. Huang, X. Yang, D. Mei, and Z. Chen, “Unified contact model and hybrid motion/force control for teleoperated manipulation in unknown environments,” IEEE/ASME Transactions on Mechatronics , vol. 30, no. 2, pp. 921–932, Apr. 2025

    work page 2025

  4. [4]

    Design and modeling of a smart torque-adjustable rotary electroadhesive clutch for application in human–robot interaction,

    N. Feizi, S. F. Atashzar, M. R. Kermani, and R. V . Patel, “Design and modeling of a smart torque-adjustable rotary electroadhesive clutch for application in human–robot interaction,” IEEE/ASME Transactions on Mechatronics, vol. 28, no. 5, pp. 2738–2748, Oct. 2023

    work page 2023

  5. [5]

    High fi- delity force feedback facilitates manual injection in biological samples,

    A. Mohand-Ousaid, S. Haliyo, S. R ´egnier, and V . Hayward, “High fi- delity force feedback facilitates manual injection in biological samples,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 1758–1763, Apr. 2020

    work page 2020

  6. [6]

    Influence of visual and haptic feedback on the detection of threshold forces in a surgical grasping task,

    J.-J. Cabibihan, A. Y . Alhaddad, T. Gulrez, and W. J. Yoon, “Influence of visual and haptic feedback on the detection of threshold forces in a surgical grasping task,” IEEE Robotics and Automation Letters , vol. 6, no. 3, pp. 5525–5532, Jul. 2021

    work page 2021

  7. [7]

    Rml glove—an exoskeleton glove mechanism with haptics feedback,

    Z. Ma and P. Ben-Tzvi, “Rml glove—an exoskeleton glove mechanism with haptics feedback,” IEEE/ASME Transactions on Mechatronics , vol. 20, no. 2, pp. 641–652, Apr. 2015

    work page 2015

  8. [8]

    Wres: A novel 3 dof wrist exoskeleton with tendon- driven differential transmission for neuro-rehabilitation and teleopera- tion,

    D. Buongiorno, E. Sotgiu, D. Leonardis, S. Marcheschi, M. Solazzi, and A. Frisoli, “Wres: A novel 3 dof wrist exoskeleton with tendon- driven differential transmission for neuro-rehabilitation and teleopera- tion,” IEEE Robotics and Automation Letters , vol. 3, no. 3, pp. 2152– 2159, Jul. 2018

    work page 2018

Show all 28 references
  1. [9]

    Nonlinearity compensation in a multi-dof shoulder sensing exosuit for real-time teleoperation,

    R. J. Varghese, A. Nguyen, E. Burdet, G.-Z. Yang, and B. P. L. Lo, “Nonlinearity compensation in a multi-dof shoulder sensing exosuit for real-time teleoperation,” in Proc. 3rd IEEE Int. Conf. Soft Robot. (RoboSoft), 2020, pp. 668–675

    2020

  2. [10]

    Homie: Humanoid loco-manipulation with isomorphic exoskeleton cockpit,

    Q. Ben, F. Jia, J. Zeng, J. Dong, D. Lin, and J. Pang, “Homie: Humanoid loco-manipulation with isomorphic exoskeleton cockpit,” arXiv preprint arXiv:2502.13013, 2025, available: https://arxiv.org/abs/2502.13013

    2025 arXiv

  3. [11]

    A tactile lightweight exoskeleton for teleoperation: Design and control performance,

    M. Forouhar, H. Sadeghian, D. P. Suay, A. Naceri, and S. Haddadin, “A tactile lightweight exoskeleton for teleoperation: Design and control performance,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2024, pp. 178–183

    2024

  4. [12]

    A heterogeneous master-slave teleoperation method for 7-dof manipulator,

    R. Su, K. Xu, L. Zhao, and P. Yu, “A heterogeneous master-slave teleoperation method for 7-dof manipulator,” in 2021 China Automation Congress (CAC), 2021, pp. 1740–1744

    2021

  5. [13]

    Haptiknit: Dis- tributed stiffness knitting for wearable haptics,

    C. du Pasquier, S. P. Chockalingam, H. Ishii et al. , “Haptiknit: Dis- tributed stiffness knitting for wearable haptics,” Science Robotics, vol. 9, p. eado3887, 2024

    2024

  6. [14]

    Soft pneumatic elbow ex- oskeleton reduces the muscle activity, metabolic cost and fatigue during holding and carrying of loads,

    J. Nassour, G. Zhao, and M. Grimmer, “Soft pneumatic elbow ex- oskeleton reduces the muscle activity, metabolic cost and fatigue during holding and carrying of loads,” Scientific Reports , vol. 11, p. 12556, July 2021

    2021

  7. [15]

    Intuitive control of a robotic arm and hand system with pneumatic haptic feedback,

    S. Li, R. Rameshwar, A. M. V otta, and C. D. Onal, “Intuitive control of a robotic arm and hand system with pneumatic haptic feedback,” IEEE Robotics and Automation Letters , vol. 4, no. 4, pp. 4424–4430, October 2019

    2019

  8. [16]

    Evaluation of an exoskeleton-based bimanual teleoperation architecture with independently passivated slave devices,

    F. Porcini, D. Chiaradia, S. Marcheschi, M. Solazzi, and A. Frisoli, “Evaluation of an exoskeleton-based bimanual teleoperation architecture with independently passivated slave devices,” inProceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA) ...

    2020

  9. [17]

    Design of an electrically actuated lower extremity exoskeleton,

    A. Zoss and H. Kazerooni, “Design of an electrically actuated lower extremity exoskeleton,” Advanced Robotics, vol. 20, no. 9, pp. 967–988, 2006

    2006

  10. [18]

    Magnetorheological characterization of ptfe-based grease with mos2 additive at different temperatures,

    A. Raj, C. Sarkar, and M. Pathak, “Magnetorheological characterization of ptfe-based grease with mos2 additive at different temperatures,” IEEE Transactions on Magnetics, vol. 57, no. 7, pp. 1–10, July 2021

    2021

  11. [19]

    Magnetorheology: Materials and application,

    B. J. Park, F. F. Fang, and H. J. Choi, “Magnetorheology: Materials and application,” Soft Matter, vol. 6, pp. 5246–5253, 2010

    2010

  12. [20]

    Magnetorhe- ological fluids: a review,

    J. D. Vicente, D. J. Klingenberg, and R. Hidalgo-Alvarez, “Magnetorhe- ological fluids: a review,” Soft Matter , vol. 7, no. 8, pp. 3701–3710, 2011

    2011

  13. [21]

    Mr fluids, foam and elastomer devices,

    J. D. Carlson and M. R. Jolly, “Mr fluids, foam and elastomer devices,” Mechatronics, vol. 10, pp. 555–569, 2000

    2000

  14. [22]

    Design and validation of a compatible 3-degrees of freedom shoulder exoskeleton with an adaptive center of rotation,

    H. Yan, C. Yang, Y . Zhang, and Y . Wang, “Design and validation of a compatible 3-degrees of freedom shoulder exoskeleton with an adaptive center of rotation,” Journal of Mechanical Design , vol. 136, no. 7, p. 071006, Jul 2014

    2014

  15. [23]

    Haptic glove with mr brakes for virtual reality,

    J. Blake and H. B. Gurocak, “Haptic glove with mr brakes for virtual reality,” IEEE/ASME Transactions on Mechatronics , vol. 14, no. 5, pp. 606–615, Oct 2009

    2009

  16. [24]

    Performance evaluation of a hollowed multi-drum magnetorheological brake based on finite element analysis considering hollow casing radius,

    H. Qin, A. Song, and Y . Mo, “Performance evaluation of a hollowed multi-drum magnetorheological brake based on finite element analysis considering hollow casing radius,” IEEE Access , vol. 7, pp. 96 070– 96 078, 2019

    2019

  17. [25]

    Efficient and precise homo-hetero teleoperation based on an optimized upper limb exoskeleton,

    C. Cheng, W. Dai, T. Wu, X. Chen, M. Wu, J. Yu, J. Jiang, and H. Lu, “Efficient and precise homo-hetero teleoperation based on an optimized upper limb exoskeleton,” IEEE/ASME Transactions on Mechatronics , pp. 1–13, 2024

    2024

  18. [26]

    Emg versus torque control of human–machine systems: Equalizing control signal variability does not equalize error or uncertainty,

    R. E. Johnson, K. P. Kording, L. J. Hargrove, and J. W. Sensinger, “Emg versus torque control of human–machine systems: Equalizing control signal variability does not equalize error or uncertainty,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, n...

    2017

  19. [2017]

    Later, she was granted as the Research Fellow of Japan Society for Promotion of Science (JSPS) with Tohoku University, Japan, from 2019 to 2020. She is currently employed with Anhui University as a full professor, with her research interests fo- cused on actuation of robots, m...

    2019

  20. [2022]

    degree in Biological Engineering Mechanics at the School of Engineering Sciences, University of Science and Technology of China

    He is currently pursuing a Ph.D. degree in Biological Engineering Mechanics at the School of Engineering Sciences, University of Science and Technology of China. His research interests include quadruped robot control systems and the design of magnetorheological actuator system...

    2024

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