cs.ROAug 24, 2026

Design of a Biomimetic Joint-Covering Skin with Tissue-Like Structure to Enhance Proprioception in a Musculoskeletal Humanoid

Authors: Akihiro Miki, Shun Hasegawa, Yoshimoto Ribayashi, Kento Kawaharazuka, Kei Okada

Organizations: Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan · AI Center, Graduate School of Information Science and Technology, The University of Tokyo, Japan

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Abstract

Proprioception in musculoskeletal humanoids is typically estimated primarily from muscle sensing, while the role of cutaneous deformation around joints remains insufficiently explored. In biological systems, mechanoreceptors distributed within soft tissue complement muscle feedback and support reliable joint state estimation. This study presents the design of a biomimetic joint-covering skin with a tissue-like layered structure that integrates pressure- and stretch-sensitive elements within the joint-covering tissue. The proposed skin is implemented on the musculoskeletal humanoid Musashi-W, and its independent proprioceptive capability as well as its integration with muscle sensing are evaluated. Experimental results show that the proposed skin alone achieves joint angle estimation with an average error of approximately 3 degrees. Furthermore, integration with muscle sensing improves estimation accuracy. Owing to its joint-covering structure, the skin may mechanically mitigate the influence of external disturbances on the muscles, and the integration of multiple modalities suggests the possibility of contributing to the identification of external stimuli that are difficult to interpret using muscle sensing alone. This work presents a design methodology for biomimetic joint-covering skin and demonstrates that such tissue-structured skin can serve as an effective approach for extending proprioceptive systems in musculoskeletal humanoids.

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Fig. 1: Overview of the proposed tissue-structured biomimetic joint-covering skin system. a. Concept of the tissue-structured biomimetic skin. The layered structure of biological skin (epidermis, dermis, and subcutaneous tissue) and its mechanoreceptors are mimicked by a multilayer soft structure embedding receptor-like sensors, including strain gauge sensors corresponding to Merkel cells and conductive filament structures corresponding to Ruffini endings. b. Implementation on the musculoskeletal humanoid Musashi-W. (1) The conventional joint module is replaced with a biomimetic joint structure adapted from Miki et al. [ 5 ] under the CC BY 4.0 license. (2) The joint region is enclosed by the tissue-structured biomimetic skin. (3) The resulting joint-skin structure is implemented on the musculoskeletal humanoid Musashi-W for enhancing proprioception.
Fig. 2: Fabrication and integration process of the tissue-structured biomimetic joint-covering skin. a. Strain gauge sensors used as Merkel cell-like elements. b. Conductive chainmail sensors used as stretch-sensitive elements corresponding to Ruffini endings. c. Illustration of the sensor arrangement in the biomimetic skin, showing the spatial distribution of strain gauge sensors and conductive chainmail sensors. Sensors are placed with reference to biological tissue organization and embedded as densely as space permits. d. Fabrication procedure: 3D printing of the mold, embedding strain gauge sensors during dermal layer fabrication, and embedding conductive chainmail sensors during subcutaneous layer fabrication. A pair of skin components is then fabricated and combined. The epidermal layer is applied by thin coating, the biomimetic joint is enclosed, and the structure is finally mounted onto the musculoskeletal humanoid. The biomimetic joint is adapted from Miki et al. [ 5 ] under the CC BY 4.0 license.
Fig. 3: Process of sensorimotor data collection using the musculoskeletal humanoid equipped with the tissue-structured biomimetic joint-covering skin. During robot motion, muscle-related signals (muscle length and muscle tension), cutaneous signals from distributed strain gauges (Merkel-cell-like sensors) and conductive sensors (Ruffini-like sensors), and motion capture measurements are recorded simultaneously. These signals are used to construct a sensorimotor dataset for evaluating proprioceptive estimation.
Fig. 4: Comparison of single-modality joint angle estimation performance for the elbow (3DOF). RMSE are averaged across the three joint angles and paired by random seed ( n=20n=20 ). ML: muscle length, SKIN-R: Ruffini-like skin sensor, SKIN-M: Merkel-like skin sensor, MT: muscle tension.
ModalitynnMean RMSESDMedian RMSE
Muscle length (ML)201.700.121.70
Ruffini-like Skin Sensor (SKIN-R)202.560.142.52
Merkel-like Skin Sensor (SKIN-M)202.690.442.58
Muscle tension (MT)204.840.254.78
TABLE I: Summary of elbow joint angle estimation errors for single-modality conditions.
ComparisonMean difference (ML – X)pppHolmp_{\mathrm{Holm}}
ML vs MT−3.14-3.14<10−5<10^{-5}<0.001<0.001
ML vs SKIN-M−0.99-0.99<10−5<10^{-5}<0.001<0.001
ML vs SKIN-R−0.86-0.86<10−5<10^{-5}<0.001<0.001
TABLE II: Paired statistical comparison between muscle length and other modalities (Wilcoxon signed-rank test, n=20n=20 ).
Fig. 5: Comparison of multimodal proprioception estimation performance for the elbow (three degrees of freedom). RMSE values are averaged across targets and paired by random seed ( n=20n=20 ). Results are shown for different modality combinations and fusion architectures.
MethodnnMean RMSESDMedian RMSE
M_fused (concat)202.030.192.01
M_fused (enc)201.760.141.74
T_fused (concat)202.480.222.45
T_fused (enc)202.150.202.12
M+T (concat)202.050.172.02
M+T (enc)201.620.111.60
TABLE III: Summary of elbow joint angle estimation errors for multimodal fusion conditions.
ComparisonMean diff. (ML–Fusion)pppHolmp_{\mathrm{Holm}}
ML vs M_fused (concat)−0.334-0.3342.0×10−62.0\times 10^{-6}1.1×10−51.1\times 10^{-5}
ML vs T_fused (concat)−0.781-0.7812.0×10−62.0\times 10^{-6}1.1×10−51.1\times 10^{-5}
ML vs T_fused (enc)−0.446-0.4462.0×10−62.0\times 10^{-6}1.1×10−51.1\times 10^{-5}
ML vs M+T (concat)−0.353-0.3532.0×10−62.0\times 10^{-6}1.1×10−51.1\times 10^{-5}
ML vs M_fused (enc)−0.061-0.0611.0×10−31.0\times 10^{-3}1.0×10−31.0\times 10^{-3}
ML vs M+T (enc)+0.077\mathbf{+0.077}3.2×10−43.2\times 10^{-4}6.5×10−46.5\times 10^{-4}
TABLE IV: Paired statistical comparison between the muscle length modality and multimodal fusion conditions (Wilcoxon signed-rank test, n=20n=20 ).
Fig. 6: External mechanical stimulation experiment. a. External perturbation applied to the elbow region using a rod during robot motion. b. External perturbation applied manually by hand. c. Comparison of joint angle estimation errors (RMSE) across sensing modalities under external stimulation. RMSE values are averaged across the three elbow joint angles and paired by random seed ( n=20n=20 ). ML: muscle length, SKIN-M: Merkel-like skin sensor, SKIN-R: Ruffini-like skin sensor.
ModalityNo-stimStimpHolmp_{\mathrm{Holm}}
Muscle length (ML)1.70±0.121.70\pm 0.124.18±0.074.18\pm 0.07<0.001<0.001
Ruffini-like Skin Sensor (SKIN-R)2.56±0.142.56\pm 0.1453.63±1.8253.63\pm 1.82<0.001<0.001
Merkel-like Skin Sensor (SKIN-M)2.69±0.442.69\pm 0.4440.81±1.9940.81\pm 1.99<0.001<0.001
TABLE V: Effect of external mechanical stimulation on elbow joint angle estimation error (RMSE, deg).
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