arXiv cs.AI· Fabio Bonsignorio, Elisabetta Barletta, Sorin Dragomir, Francesco Esposito, Enrica Zereik·· 3 小时前AI 评分26
柔顺性为何有助于控制：基于信息度量的柔顺踝关节被动行走分析
Softness is something you also need! Why compliance helps control: information metrics of a passive walker with compliant ankle
AI 导读
arXiv 论文论证柔顺（compliance）设计有助于机器人控制，回顾信息度量与智能体动力学之间的数学结果，并提出相空间体积计算框架。
正文 · 原文
Abstract:A great gap in adaptivity, flexibility, and dexterity still exists between intelligent robots, whether they are made of rigid or (partially) compliant components, and their naturally evolved animal and vegetal counterparts. One reason could be of `paradigm' nature. On the one hand, most robots (both rigid and soft) are still designed by following a `Cartesian' approach, with top-down control and a clear mind-body separation. Designing artificial agents on the basis of the principles that we observe in biology is not an easy task. The principles of organization of animals and plants rely on morphological computation and emergence of fit behaviors from loosely coupled networks of embodied agents.
Modeling and control require radically new approaches. In this paper we briefly recall and explain some seminal mathematical results linking information metrics and dynamics of the agents, already published years ago; we integrate them with some recent methods developed by us and by the community, we highlight some brief remarks about their relevance and we hint to our current and near future work towards finding concrete and efficient ways for the modeling and control of completely or partially compliant artificial agents and for a better understanding of physical intelligence in nature. We present a technical note summarizing phase-space volume calculations for inverted-pendulum and passive-dynamic walking models, following our proposed framework. Starting from the McGeer compass gait, we introduce knee-ankle compliance, foot-rocker effects, and a Ruina-style compass gait with distributed leg inertia. Standard heel-strike maps are derived and used in the estimation of viable phase-space volumes $W(\gamma)$, based on a state-dependent Monte-Carlo and Reproducing Kernel Hilbert Spaces methods. We show how the methods based on RKHS allow deeper and more subtle analysis.
| Comments: | main: 11 pages, 6 figures; supplemental: 7 pages, 1 figure |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.13779 [cs.RO] |
| (or arXiv:2611.13779v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13779 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Enrica Zereik [view email]
[v1]
Tue, 10 Nov 2026 09:17:17 UTC (2,727 KB)
来源：arXiv cs.AI · arxiv.org