Design of a Biomimetic Joint-Covering Skin with Tissue-Like Structure to Enhance Proprioception in a Musculoskeletal Humanoid
Biomimetic layered skin with sensor integration achieves ~3° joint angle estimation; multimodal fusion improves accuracy.
Key Findings
Methodology
The study employs a multilayer soft tissue structure mimicking human skin, embedding strain gauges and conductive fibers to simulate Merkel cells and Ruffini endings. Using 3D printing, the layers are assembled into a dense, tissue-like skin that covers a biomimetic joint. Sensors are densely embedded following biological distribution. Data from these sensors, combined with muscle length and tension signals, are fed into a multilayer perceptron (MLP) model for joint angle estimation. The training uses AdamW optimizer with RMSE as loss. The approach demonstrates that single-modality skin sensing yields about 3° error, which reduces when fused with muscle signals.
Key Results
- The biomimetic skin alone estimates joint angles with an average RMSE of approximately 3°, with errors ranging from 2.5° to 3.5°. Muscle length sensing alone achieves about 1.7° RMSE. Multimodal fusion, especially the encoder-based M+T model, reduces error further to below 2.2°, significantly outperforming single modalities (p<0.001). The dense tissue-like structure buffers external disturbances, enhancing robustness. The experiments on Musashi-W robot with motion capture data validate these findings, covering a 50° elbow range in various postures.
- The dataset comprises 615 samples, with signals from 32 Merkel-like strain gauges and 12 Ruffini-like stretch sensors, alongside muscle length and tension data. The models trained with different fusion architectures show that integrating multimodal sensory data improves estimation accuracy. The encoder-based fusion consistently outperforms simple concatenation, indicating the benefit of low-dimensional embedding in sensor fusion. These results highlight the potential of tissue-structured skin to extend proprioceptive capabilities in complex robotic systems.
- The fusion model M+T enc, combining muscle and skin information via an encoder, achieved the lowest RMSE (~2.2°), demonstrating the synergistic effect of multimodal sensing. The approach effectively mitigates external disturbances and enhances estimation stability. The findings suggest that mimicking biological tissue organization with dense sensory embedding can significantly advance robotic proprioception, enabling more natural and adaptive movements in unstructured environments.
Significance
This work advances the field of biomimetic robotics by introducing a tissue-like layered skin that integrates multiple sensory modalities, addressing the limitations of traditional single-sensor approaches. By closely mimicking biological skin’s layered structure and sensory distribution, the proposed system enhances the accuracy and robustness of joint state estimation. Such improvements are crucial for developing autonomous robots capable of complex, human-like movements and environmental interactions. The integration of multimodal sensing within a soft, tissue-structured framework opens new avenues for designing more adaptive and resilient robotic systems, with broad implications for service robots, prosthetics, and rehabilitation devices. This research bridges biological insights and engineering innovation, pushing the boundaries of what robotic proprioception can achieve.
Technical Contribution
The study introduces a novel multilayer soft tissue structure that mimics human skin, embedding receptor-like sensors at specific depths to emulate Merkel cells and Ruffini endings. The fabrication process leverages 3D printing for complex geometries, ensuring dense sensor placement within mechanically compliant layers. The integration of multiple sensing modalities within a tissue-like organization enhances the richness of proprioceptive signals. The deep learning-based fusion models, especially the encoder-based architecture, demonstrate superior performance over traditional concatenation methods, providing a new paradigm for multimodal sensor integration. This approach offers a scalable, biologically inspired framework for extending robotic sensing capabilities beyond conventional point sensors, with potential for real-time, high-fidelity joint state estimation in complex environments.
Novelty
This is the first work to implement a multilayer, tissue-structured biomimetic skin with embedded Merkel cell and Ruffini-like sensors for robotic joint proprioception. Unlike prior electronic skins that focus on tactile discrimination or sparse sensing, this approach emphasizes depth-specific sensory embedding, mimicking biological tissue organization. The dense, layered design allows for richer, more robust joint state estimation, especially in the presence of external disturbances. The use of deep learning for multimodal fusion within this tissue-like structure further distinguishes this work, providing a significant leap toward biologically plausible proprioceptive systems in robotics.
Limitations
- Material properties still differ from real biological tissue, limiting the fidelity of mechanical and sensory responses. Sensor density is constrained by fabrication complexity, potentially missing finer tactile details. The system’s robustness under extreme external disturbances or rapid movements remains untested, requiring further validation. Additionally, real-time implementation and scalability to full-body systems need exploration, as current fabrication and processing are still in experimental stages.
Future Work
Future efforts will focus on increasing sensor density and diversity, exploring adaptive and self-healing materials, and integrating real-time processing algorithms. Extending the tissue-structured design to full-body applications and dynamic environments will be prioritized. Incorporating advanced machine learning techniques, such as reinforcement learning, could further improve sensory fusion and control. Ultimately, the goal is to develop a fully autonomous, biologically inspired proprioceptive system capable of complex, adaptive behaviors in unstructured settings.
AI Executive Summary
Deep Dive
Plain Language Accessible to non-experts
想象你在厨房做饭，厨房里有很多不同的工具，比如刀、锅、铲子。每个工具都能帮你完成不同的任务，但你需要知道它们的状态，比如刀是否锋利、锅里的水多不多。传统的机器人就像只用一把刀，只能做简单的事情，不能感知工具的细节。而这项研究就像在厨房里装了很多不同的传感器，能感受到工具的压力、拉伸和位置，就像我们用手摸到热锅或硬菜一样。通过模仿人类皮肤的多层结构，机器人可以更全面地感知自己关节的角度和外界的刺激，就像人用皮肤感觉到压力和温度一样。这让机器人变得更聪明、更灵活，能在复杂环境中更好地工作，就像我们在厨房里做饭一样得心应手。
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.