ZAC-Energy: Collective Energy–Computation Coordination for Resource-Constrained Embodied Intelligence
Abstract
Energy-constrained embodied intelligent systems must allocate not only computational workloads but also the energy expenditure required to execute those workloads. Conventional local-computing designs can require each agent to perform its own inference, even when its battery state, thermal condition, mission obligations, or remaining operating time make local computation undesirable. Conversely, transferring a task to another agent may increase communication energy, processing demand, coordination overhead, and the recipient's risk of premature energy depletion. These interactions motivate a system-level approach to coordinating energy budgets and computational responsibilities across heterogeneous agents.
This paper proposes ZAC-Energy, a research framework for collective energy–computation coordination within the broader Zero-Agent-Compute (ZAC) framework. The central design principle is that computational responsibility should be assigned according to both computational capability and the current suitability of each agent to expend energy. An agent with sufficient energy reserves and available processing capacity may execute computationally intensive tasks for the group, while an energy-constrained agent preserves resources for mission-critical operations and transfers eligible noncritical workloads to other providers. The framework jointly considers battery state, energy reserves, task deadlines, computational capacity, communication costs, charging opportunities, and expected mission duration.
A formal model is developed to represent agent energy states, computational workloads, energy consumption, task assignments, charging decisions, and operational constraints. A joint optimization objective incorporates computation energy, communication energy, propulsion or mobility energy where applicable, charging costs, task failures, and mission-level consequences. A state-aware coordination mechanism is proposed to support task admission, energy-sensitive assignment, charging coordination, reassignment, and safe fallback. The framework also introduces a distinction between computational energy efficiency and mission-level energy suitability: the node that consumes the least energy for a task is not necessarily the node that should perform it when the distribution of remaining energy across the group is considered.
ZAC-Energy is presented as a proposed framework and a set of testable hypotheses rather than an experimentally validated system. Its potential contribution must be established through comparisons with energy-unaware scheduling, conventional energy-aware offloading, fixed-threshold policies, and joint energy–computation optimization baselines. The paper identifies the conditions under which collective coordination may improve task completion, energy balance, and mission endurance, as well as situations in which local execution is preferable because of communication costs, deadlines, safety requirements, or insufficient provider capacity.
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BibTeX
@misc{zhang2026zacenergy,
title = {{ZAC-Energy: Collective Energy–Computation Coordination for Resource-Constrained Embodied Intelligence}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23450988},
url = {https://zenodo.org/records/23450988},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - ZAC-Energy: Collective Energy–Computation Coordination for Resource-Constrained Embodied Intelligence
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/18
PB - Zenodo
DO - 10.5281/zenodo.23450988
UR - https://zenodo.org/records/23450988
AB - Energy-constrained embodied intelligent systems must allocate not only computational workloads but also the energy expenditure required to execute those workloads. Conventional local-computing designs can require each agent to perform its own inference, even when its battery state, thermal condition, mission obligations, or remaining operating time make local computation undesirable. Conversely, transferring a task to another agent may increase communication energy, processing demand, coordination overhead, and the recipient's risk of premature energy depletion. These interactions motivate a system-level approach to coordinating energy budgets and computational responsibilities across heterogeneous agents. This paper proposes ZAC-Energy, a research framework for collective energy–computation coordination within the broader Zero-Agent-Compute (ZAC) framework. The central design principle is that computational responsibility should be assigned according to both computational capability and the current suitability of each agent to expend energy. An agent with sufficient energy reserves and available processing capacity may execute computationally intensive tasks for the group, while an energy-constrained agent preserves resources for mission-critical operations and transfers eligible noncritical workloads to other providers. The framework jointly considers battery state, energy reserves, task deadlines, computational capacity, communication costs, charging opportunities, and expected mission duration. A formal model is developed to represent agent energy states, computational workloads, energy consumption, task assignments, charging decisions, and operational constraints. A joint optimization objective incorporates computation energy, communication energy, propulsion or mobility energy where applicable, charging costs, task failures, and mission-level consequences. A state-aware coordination mechanism is proposed to support task admission, energy-sensitive assignment, charging coordination, reassignment, and safe fallback. The framework also introduces a distinction between computational energy efficiency and mission-level energy suitability: the node that consumes the least energy for a task is not necessarily the node that should perform it when the distribution of remaining energy across the group is considered. ZAC-Energy is presented as a proposed framework and a set of testable hypotheses rather than an experimentally validated system. Its potential contribution must be established through comparisons with energy-unaware scheduling, conventional energy-aware offloading, fixed-threshold policies, and joint energy–computation optimization baselines. The paper identifies the conditions under which collective coordination may improve task completion, energy balance, and mission endurance, as well as situations in which local execution is preferable because of communication costs, deadlines, safety requirements, or insufficient provider capacity.
ER -