# 通过与复杂地形的全身交互进行感知：来自机器人模型的物理洞见

> 原文链接：https://robotics.hellobike.com/papers/2026-10-11-2610-15334
> 频道：papers | 更新日期：2026-10-11 | 标签：arxiv, cs.RO
> 核心摘要：本文探讨生物启发机器人物理模型如何识别复杂地形全身交互产生的机械信息，并以三个案例验证机制与行为益处。

# 通过与复杂地形的全身交互进行感知：来自机器人模型的物理洞见

::: v-pre
> 原文链接：[Sensing via whole-body interactions with complex terrains: physical insights from robotic models](https://arxiv.org/abs/2610.15334)（arXiv 2610.15334 · 发布日期：2026-10-11）

**Sensing via whole-body interactions with complex terrains: physical insights from robotic models**

本文探讨生物启发机器人物理模型如何识别复杂地形全身交互产生的机械信息，并以三个案例验证机制与行为益处。

## 核心要点

- 动物在复杂地形中产生全身力、形变和振动，但运动持续改变信息来源，难以解析。
- 作者提出用生物学指导的机器人物理模型，识别候选机械信息并测试物理机制。
- 三个案例涵盖密集杂乱穿越、崎岖地形推进和蛛网上猎物振动感知。
- 机器人与动物的对应性支持机制的生物合理性，并推动更好测量和建模。

**验证范围与局限**：验证范围为三个机器人—动物对应案例，仍需测量和模型确定信息如何被感知并调制行为。

## 英文摘要 (Abstract)

In nature, animals interact with complex terrains and often generate forces, deformations, and vibrations across the body and environment. Such interaction-induced mechanical information may guide behavior, yet its generation and use are difficult to resolve because movement rapidly and continually reconfigures the interactions that produce it. Sensory neurophysiology and biomechanics have revealed how mechanical information is transformed within sensory, neural, and musculoskeletal subsystems, whereas studies in complex terrains reveal rich whole-body interactions and behavioral outcomes. Connecting these levels remains a challenge. Here, I present a focused perspective on how biologically guided robotic physical models can help identify candidate mechanical information generated through whole-body interactions, test its physical mechanisms and behavioral benefits, and generate biological hypotheses. Three case studies illustrate this approach, multi-legged locomotor transitions to traverse dense clutter, limbless vertical body bending to propel through uneven terrain, and rapid spider leg crouching to help vibration sensing of prey on a web. Across these systems, correspondence between robots and animals supports the biological plausibility of these candidate physical mechanisms and motivates better measurements and models to determine how the relevant mechanical information is sensed and used to modulate behavior.

作者：Chen Li
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## 原始参考来源 (Sources)

- [arXiv 2610.15334：Sensing via whole-body interactions with complex terrains: physical insights from robotic models](https://arxiv.org/abs/2610.15334)