PIPHEN: Physical Interaction Prediction with Hamiltonian Energy Networks

2025-11-20

AI Summary

Multi-robot complex physical collaboration faces the “shared-brain dilemma”: high-dimensional perceptual data (e.g., 30 MB/s video streams) induce bandwidth bottlenecks and decision latency. To address this, we propose a distributed physical cognitive control framework. Our method comprises three core components: (1) edge-side semantic distillation, compressing raw sensory inputs into compact, interpretable, structured physical representations; (2) a Physics Interaction Prediction Network (PIPN), built via large-model knowledge distillation, enabling semantic-level collaborative reasoning; and (3) a Hamiltonian Energy Network (HEN) controller, enforcing action coordination accuracy through energy conservation principles. Experiments demonstrate that our approach reduces information volume to under 5% of the original size, slashes collaborative decision latency from 315 ms to 76 ms, and significantly improves task success rate.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Low Level & Physics-based Vision
• Multiagent Systems: Coordination and Collaboration

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Address bandwidth bottlenecks in multi-robot physical collaborations
• Replace raw data transmission with compact semantic communication
• Reduce decision latency while improving task success rates

Innovation

• Replaces raw data communication with semantic communication
• Uses Physical Interaction Prediction Network for representation
• Employs Hamiltonian Energy Network for coordinated actions