PIPHEN: Physical Interaction Prediction with Hamiltonian Energy Networks

TL;DR

PIPHEN introduces a novel framework for multi-robot physical collaboration that significantly reduces data transmission and decision latency by using semantic representations and energy-based control, improving efficiency and success rates.

Contribution

The paper presents PIPHEN, a new distributed cognition-control framework combining semantic distillation with Hamiltonian energy networks for efficient multi-robot coordination.

Findings

Data compression reduced to less than 5% of original volume.

Decision-making latency decreased from 315ms to 76ms.

Enhanced task success rates in physical robot collaborations.

Abstract

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.