气质工程：设计机器人群体中的策略性行为多样性

中文摘要 AI 辅助

没有两个机器人是完全相同的：校准、电池状态、传感器漂移和磨损使每个群体呈现出行为分布而非单一行为点，这通常被视为需要最小化的缺陷。在动物群体中，情况恰恰相反：行为上一致的个体差异（即“气质”）由自然选择塑造，且往往对群体表现具有决定性作用。本视角提出“气质工程”，这是一种仿生框架，将群体的气质分布（而非个体控制器）作为设计对象。该框架借鉴了动物气质的五个经进化验证的轴（害羞-大胆、探索-回避、活动性、攻击性和社交性）作为设计词汇，并将每个轴表示为控制器之上的连续控制参数τ∈[0,1]，可通过模块阈值、多智能体强化学习中的策略条件向量或基础模型规划器上的约束来实现。一个三阶段工作流程将任务成功标准映射到相关轴，规划τ分布的形状，并调整控制气质如何响应环境线索的反应规范。该方法的收益在去中心化条件下最为显著：当中央规划器可以在线重新分配行为时，气质分布是规划器的输出；但在没有全局知识的群体中，它必须是离线的、预期性的设计输入。行为和平台异质性因此成为协同设计变量，我初步提出了源于机器人特有而动物不具备特征的机器人原生轴（自模型可塑性、力量性、主动性和表达性）。工程化异质性已被证明在聚合和探索等任务中优于同质群体；确定何时以及多大程度的异质性值得其成本，是该领域现在可以推进的工作。

英文摘要

No two robots are truly identical: calibration, battery state, sensor drift and wear give every swarm a distribution of behaviour rather than a single point, usually treated as an imperfection to be minimised. In animal collectives the reverse holds: consistent individual differences in behaviour ('temperament') are shaped by natural selection and often decisive for group performance. This perspective proposes 'temperament engineering', a bio-inspired framework that treats the swarm's distribution of temperaments, rather than the individual controller, as the design object. It borrows five evolutionarily validated axes of animal temperament (shyness-boldness, exploration-avoidance, activity, aggressiveness and sociability) as a design vocabulary, rendering each as a continuous control parameter $τ\in [0,1]$ above the controller, realisable as a module threshold, a policy-conditioning vector in multi-agent reinforcement learning, or a constraint on a foundation-model planner. A three-phase workflow maps mission success criteria onto relevant axes, plans the shape of the $τ$ distribution, and tunes reaction norms governing how temperament responds to environmental cues. The payoff is greatest under decentralisation: where a central planner can reassign behaviour online, a temperament distribution is a planner output, but in a swarm without global knowledge it must be an offline, anticipatory design input. Behavioural and platform heterogeneity are thereby co-design variables, and I sketch tentative robot-native axes (self-model plasticity, forcefulness, initiative and expressiveness) arising from features robots have and animals do not. Engineered heterogeneity has been shown to outperform homogeneous swarms in tasks such as aggregation and exploration; establishing when, and how much, heterogeneity repays its cost is the work the field can now take forward.

发表机构

• University of Bristol（布里斯托大学）

机构由 AI 辅助整理，请以论文原文为准。