用物理能量网络压缩机器人感知数据，实现低延迟协同决策。
PIPHEN: Physical Interaction Prediction with Hamiltonian Energy Networks
- 在机器人边缘进行语义蒸馏，将原始数据转为紧凑物理表征。
- 信息量压缩至原数据5%以下，决策延迟从315毫秒降至76毫秒。
- 适合资源受限的多机器人协同系统，提升任务成功率。
复杂物理协作中的多机器人系统面临“共享大脑困境”：传输高维多媒体数据（如每秒约30MB的视频流）导致严重带宽瓶颈和决策延迟。为此，我们提出PIPHEN——一种分布式物理认知-控制框架。核心思想是在机器人边缘执行“语义蒸馏”，将高维感知数据重构为紧凑、结构化的物理表征，替代原始数据通信。该思路通过两个关键组件实现：(1) 基于大模型知识蒸馏的物理交互预测网络（PIPN），生成表征；(2) 基于能量守恒的哈密顿能量网络（HEN）控制器，精准将表征转化为协同动作。实验表明，相比基线方法，PIPHEN可将信息表征压缩至原始数据体积的5%以下，将协同决策延迟从315毫秒降至76毫秒，同时显著提升任务成功率。该工作为资源受限的多机器人系统提供了一种根本高效的解决方案。
原文摘要 · Abstract (English)
Multi-robot systems in complex physical collaborations face a "shared brain dilemma": transmitting high-dimensional multimedia data (e.g., video streams at ~30MB/s) creates severe bandwidth bottlenecks and decision-making latency. To address this, we propose PIPHEN, an innovative distributed physical cognition-control framework. Its core idea is to replace "raw data communication" with "semantic communication" by performing "semantic distillation" at the robot edge, reconstructing high-dimensional perceptual data into compact, structured physical representations. This idea is primarily realized through two key components: (1) a novel Physical Interaction Prediction Network (PIPN), derived from large model knowledge distillation, to generate this representation; and (2) a Hamiltonian Energy Network (HEN) controller, based on energy conservation, to precisely translate this representation into coordinated actions. Experiments show that, compared to baseline methods, PIPHEN can compress the information representation to less than 5% of the original data volume and reduce collaborative decision-making latency from 315ms to 76ms, while significantly improving task success rates. This work provides a fundamentally efficient paradigm for resolving the "shared brain dilemma" in resource-constrained multi-robot systems.
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