Proprioception is the "sixth sense" that detects limb postures with motor neurons. It requires a natural integration between the musculoskeletal systems and sensory receptors, which is challenging among modern robots that aim for lightweight, adaptive, and sensitive designs at a low cost. Here, we present the Soft Polyhedral Network with an embedded vision for physical interactions, capable of adaptive kinesthesia and viscoelastic proprioception by learning kinetic features. This design enables passive adaptations to omni-directional interactions, visually captured by a miniature high-speed motion tracking system embedded inside for proprioceptive learning. The results show that the soft network can infer real-time 6D forces and torques with accuracies of 0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also incorporate viscoelasticity in proprioception during static adaptation by adding a creep and relaxation modifier to refine the predicted results. The proposed soft network combines simplicity in design, omni-adaptation, and proprioceptive sensing with high accuracy, making it a versatile solution for robotics at a low cost with more than 1 million use cycles for tasks such as sensitive and competitive grasping, and touch-based geometry reconstruction. This study offers new insights into vision-based proprioception for soft robots in adaptive grasping, soft manipulation, and human-robot interaction.

翻译：本体感知是通过运动神经元检测肢体姿态的“第六感”，它要求肌肉骨骼系统与感觉受体之间的自然整合，这对追求轻量化、自适应、低成本且高灵敏度的现代机器人构成挑战。本文提出了一种嵌入视觉感知的软多面体网络，通过学习动力学特征实现自适应动觉与粘弹性本体感知。该设计能够被动适应全向性交互，其内部嵌入的微型高速运动追踪系统可捕捉动态交互行为，从而实现本体感知学习。实验结果表明：在动态交互中，该软网络可实时推断六维力与力矩，精度分别达到0.25/0.24/0.35牛和0.025/0.034/0.006牛米。此外，我们通过在静态适应过程中引入蠕变与松弛修正器，将粘弹性特性融入本体感知，以优化预测结果。所提出的软网络兼具设计简洁性、全向适应性与高精度本体感知能力，是一种低成本的通用机器人解决方案，在超过100万次使用周期中可完成灵敏竞争性抓取、基于触觉的几何重构等任务。本研究为软体机器人在自适应抓取、柔性操控与人机交互中实现基于视觉的本体感知提供了新思路。