Embodied Operators and Benchmarking: Toward Reusable and Deployable Embodied Intelligence Systems
Quick Answer
The paper introduces embodied operators as modular components for embodied intelligence systems, emphasizing their reusability and deployability.
Quick Take
It presents a taxonomy of five operator categories and proposes a multi-dimensional benchmark framework to evaluate their performance across various metrics, aiming to enhance the scalability and verifiability of these systems in real-world applications.
Key Points
- Defines embodied operators as reusable modules in embodied intelligence pipelines.
- Constructs a taxonomy with five operator categories for structured representation.
- Proposes a benchmark framework evaluating correctness, efficiency, and reliability.
- Addresses challenges in operator composition, data standardization, and deployment.
- Aims to optimize embodied operators as holistic deployable components.
Paper Resources
📖 Reader Mode
~2 min readAuthors:Junwu Xiong, Jiaxuan Gao, Wei Chai, Renxing Chen, Yuzhen Li, Yu Guo, Yucheng Guo, Mingxi Luo, Wenyang Ma, Yiyun Mou, Yifei Zhang, Chen Zhou, Yongjian Guo
Abstract:Embodied intelligence systems require not only end-to-end policy models, but also reusable functional modules that transform multimodal observations, robot states, human demonstrations, and task contexts into structured representations, decisions, trajectories, control references, and system services. This work defines these modules as embodied operators and studies them as independent yet composable units in embodied intelligence pipelines. We clarify their definition boundary, emphasizing task semantics, standardized input-output contracts, deployability, reusability, and multi-layer optimizability. We further construct a taxonomy covering five categories: detection and segmentation, spatial localization and 3D understanding, hand motion recovery, embodied foundation models and task-decision operators, and planning, control, and system support operators. For each category, we summarize representative functions, technical paradigms, application roles, and practical limitations. Beyond taxonomy, we propose a multi-dimensional benchmark framework that evaluates embodied operators in terms of correctness, end-to-end efficiency, resource usage, temporal stability, portability, interface compatibility, deployment reliability, and downstream task utility. We also discuss workflow-level operator acceleration and open challenges in operator composition, data standardization, world models, VLA safety, edge deployment, and real-world application value. Overall, this work argues that embodied operators should be optimized and evaluated as holistic deployable components, providing a foundation for reusable, scalable, and verifiable embodied intelligence systems.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.03283 [cs.AI] |
| (or arXiv:2607.03283v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.03283 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jiaxuan Gao [view email]
[v1]
Fri, 3 Jul 2026 12:51:55 UTC (10,220 KB)
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
HOBA (Hierarchical On-policy Bidding Agents) is a novel hierarchical reinforcement learning framework that enhances online advertising bidding systems by improving adaptability and reducing hyperparameter tuning costs. It utilizes a for hyperparameter inference, a SARSA agent for expert model selection, and a dynamic expert pool for bid execution, achieving a +3.6% increase in target cost during large-scale deployment and outperforming state-of-the-art baselines on AuctionNet.