迈向具备任务能力的活性物质：通过碰撞学习在受限群体中避免堵塞
Toward Task Capable Active Matter: Learning to Avoid Clogging in Confined Collectives via Collisions
AI总结：
该研究针对高密度受限群体的堵塞问题，提出基于碰撞和隧道长度噪声估计的局部反转概率自适应学习规则，通过机器人物理挖掘实验验证其可有效缓解堵塞，为致密生物与机器人活性物质研究提供新思路。
摘要：
构筑由隧道和巢室组成巢穴的社会性生物必然要在受限且拥挤的环境中行进。与鸟群、昆虫群这类以流体力学和统计现象为主导的低密度群体不同，要理解高密度群体中的堵塞行为，玻璃态和过冷流体的物理学原理至关重要。我们此前的研究揭示，在受限隧道中行进的火蚁会采用 workload 分配不均、自发方向反转、有限互动时间等多样行为来缓解阻塞和卡滞，从而维持功能性流动；在小型机器人物理集群中实施类似规则，可通过堵塞团和簇的自发解散实现高性能。然而，昆虫如何习得这类行为，以及我们如何在这类场景中开发“具备任务能力”的活性物质，仍是一项挑战，部分原因在于互动动力学由局部且耗时的碰撞主导，没有单个智能体能够引导整个群体。在此，我们假设有效的流动和堵塞缓解完全可以通过局部学习涌现。我们让小型机器人群体在狭窄隧道中完成颗粒挖掘任务，允许它们随时间调整反转概率。初始阶段，机器人的反转概率相同，堵塞十分常见。反转行为改善了流动。当反转概率通过碰撞和带噪声的隧道长度估计进行自适应调整时，workload 不均性和性能均得到提升。我们对挖掘集群的机器人物理研究表明，尽管任务看似复杂困难，简单的学习规则能够缓解或利用具备任务能力的致密活性物质中不可避免的特征，为致密生物集群和机器人集群研究提供了假设。
英文摘要：
Social organisms which construct nests consisting of tunnels and chambers necessarily navigate confined and crowded conditions. Unlike low-density collectives like bird flocks and insect swarms, in which hydrodynamic and statistical phenomena dominate, the physics of glasses and supercooled fluids is important to understand clogging behaviors in high-density collectives. Our previous work revealed that fire ants flowing in confined tunnels utilize diverse behaviors like unequal workload distributions, spontaneous direction reversals, and limited interaction times to mitigate clogging and jamming and thus maintain functional flow; implementation of similar rules in a small robophysical swarm led to high performance through spontaneous dissolution of clogs and clusters. However, how the insects learn such behaviors, and how we can develop "task capable" active matter in such regimes, remains a challenge in part because interaction dynamics are dominated by local, time-consuming collisions and no single agent can guide the entire collective. Here, we hypothesized that effective flow and clog mitigation could emerge purely through local learning. We tasked small groups of robots with pellet excavation in a narrow tunnel, allowing them to modify reversal probabilities over time. Initially, robots had equal probabilities and clogs were common. Reversals improved flow. When reversal probabilities adapted via collisions and noisy tunnel length estimates, workload inequality and performance improved. Our robophysical study of an excavating swarm shows that, despite the seeming complexity and difficulty of the task, simple learning rules can mitigate or leverage unavoidable features in task-capable dense active matter, leading to hypotheses for dense biological and robotic swarms.
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