PIPHEN: Physical Interaction Prediction with Hamiltonian Energy Networks

TL;DR

PIPHEN tackles the bandwidth and latency bottleneck in multi-robot collaboration by replacing raw perceptual data with semantic knowledge distilled at the edge. It introduces PIPN to produce a hybrid physical representation and predict dynamics, and HEN to generate energy-conserving control, forming a closed perceptual-cognition-control loop. The three-stage Generate-Purify-Deploy knowledge transformation, edge-empowered cognition, and distributed communication yield data compression to under 5% of raw input and latency reductions from 315 ms to 76 ms, while boosting task success and stability. The approach demonstrates strong performance on MAP-THOR and SAR benchmarks, with successful sim-to-real transfer on XLeRobot platforms, highlighting practical impact for resource-constrained multi-robot systems.

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.

Figures (5)

• Figure 1: The overall architecture of the PIPHEN framework, comprising two core components: the Physical Cognition Engine (PIPN) and the Physics-Constrained Controller (HEN). The PIPN (top half) is responsible for distilling multi-modal sensory inputs (e.g., RGB-D video, force data, and point clouds) into a compact, structured physical representation, and predicting future physical states and their uncertainties through spatio-temporal modeling. The HEN (bottom half) receives this representation as input and, based on the principle of Hamiltonian energy conservation ($dH/dt \approx 0$), generates physically consistent and stable collaborative control commands. The entire process showcases a complete closed loop from high-dimensional raw perception to low-dimensional semantic knowledge, and then to precise physical control, aimed at efficiently resolving the "shared brain dilemma" in multi-robot systems.
• Figure 2: Comparison of PIPHEN and LLaMAR's performance on the spatial reasoning task: "Put the plate, mug, and bowl in the fridge" execution process. The top row shows LLaMAR's process: when one agent repeatedly fails due to spatial obstruction, the other agent completes multiple sub-tasks consecutively, leading to a severe workload imbalance (Balance B=0.33). The bottom row shows PIPHEN's process: through precise physical space modeling, the two agents can predict and avoid spatial conflicts, achieving a more balanced task allocation (Balance B=0.85). This comparison clearly demonstrates the significant impact of physical perception capabilities on the efficiency of multi-agent collaboration.
• Figure 3: Real-world deployment effect of the PIPHEN framework: a complete process of two XLeRobot single-arm mobile manipulators collaboratively completing a tableware setting task. The image sequence shows the entire process from an initial cluttered state to the final neat arrangement of four place settings, with each step demonstrating PIPHEN's efficient distributed physical cognition capabilities. The experimental results strongly prove that through PIPHEN's semantic communication mechanism, the robots, without transmitting high-dimensional video streams and relying only on sharing compact physical semantic representations (approximately 5% of the original data volume), can achieve precise and smooth multi-step collaborative operations, validating the practicality and robustness of our framework in solving the "shared brain dilemma."
• Figure 4: Qualitative comparison between PIPHEN and LLaMAR in the "Critical Stability Stacking" task. (a) The baseline method LLaMAR, lacking a precise physical model, relies on common sense to place the block in a safe central area, achieving a "conservative success". (b) Our PIPHEN, through its physics cognition engine (PIPN), accurately calculates the physical critical point of stability and successfully places the block at this limit, achieving a "precise success". This comparison vividly highlights PIPHEN's significant superiority in the depth of physical understanding and precise manipulation.
• Figure 5: The distributed micro-brain architecture of PIPHEN, featuring three hierarchical levels. The central coordination layer ("Brain") handles global PIPN knowledge fusion and task planning, while only processing compressed physical-semantic information. The local execution layer ("Cerebellum") deploys lightweight PIPNs for edge intelligence and basic HENs for real-time control, processing local multimedia data on each robot. The specialized processing layer ("Micro-brain") provides dynamic capabilities through function-specific modules (e.g., physics analysis, precise control, collaborative planning) that can be dynamically loaded/unloaded and shared among robots based on task requirements. The green and blue arrows represent the information flow between layers, while the purple connections show resource sharing across robots.