Embodied Operators and Benchmarking: Toward Reusable and Deployable Embodied Intelligence Systems

• URL: http://arxiv.org/abs/2607.03283v1
• Date: Fri, 03 Jul 2026 12:51:55 GMT
• Title: Embodied Operators and Benchmarking: Toward Reusable and Deployable Embodied Intelligence Systems
• Abstract summary: Embodied intelligence systems require reusable functional modules that transform multimodal observations, robot states, human demonstrations, and task contexts.<n>This work defines these modules as embodied operators and studies them as independent yet composable units in embodied intelligence pipelines.
• Score: 12.176736540015881
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• The Agent Operating System (AOS): A Reference Operating Architecture for Distributed Agentic Systems [0.04756202129182093]
  This paper proposes the Agent Operating System (AOS), a vendor-neutral reference operating architecture for distributed agentic systems.<n>AOS is not presented as a replacement for existing frameworks or infrastructure; it is proposed as the operating architecture through which heterogeneous components can be composed into governable reliable, observable, and interoperable agentic systems.
  arXiv  Detail & Related papers  (2026-08-04T06:50:49Z)
• From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence [54.62791372989525]
  Action models, vision-language--action policies, and world models have advanced this goal.<n>World Action Models (WAMs) are particularly promising because they connect candidate interventions with predicted consequences.<n>We review the evolution toward WAMs and organize these limitations into three coupled gaps: model roles and representations, objectives and standardization, and system composition.
  arXiv  Detail & Related papers  (2026-07-13T15:22:56Z)
• Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems [64.88044474617854]
  The rapid integration of large language model-based agents into recommender systems has driven a shift from static, ranking-based pipelines toward autonomous and interactive systems.<n>This survey introduces a unified taxonomy grounded in the level of autonomy and three core paradigms of agentic recommender systems.
  arXiv  Detail & Related papers  (2026-07-05T17:55:05Z)
• From Question Answering to Task Completion: A Survey on Agent System and Harness Design [67.8241530947325]
  This survey examines LLM-based agents through a model-harness lens.<n>We first clarify the functional definition of agents and the implementation view of an LLM-based agent as a foundation model and an execution harness.<n>We then analyze the limits of model-centric scaling, trace four paradigms of agent engineering, and decompose the execution harness into six coupled runtime responsibilities.
  arXiv  Detail & Related papers  (2026-06-14T05:40:16Z)
• OpenCompass: A Universal Evaluation Platform for Large Language Models [62.59671563145442]
  General-purpose large language models (LLMs) have become a critical link in advancing technological development.<n>Mainstream benchmark datasets face challenges such as the diversity of task types, inconsistent evaluation criteria, and fragmentation of data and processing.<n>This paper proposes and open-source Open LLMs, a one-stop, scalable, and high-concurrency general-purpose evaluation platform.
  arXiv  Detail & Related papers  (2026-05-19T02:50:11Z)
• Position: Avoid Overstretching LLMs for every Enterprise Task [3.679908170761854]
  We argue that AI systems should treat language models as interfaces rather than monolithic engines.<n>Our theoretical evidences show that finite computation models cannot fully capture the breadth of knowledge required for enterprise tasks.<n>Building on this, we take the position that language models should primarily be used for structured extraction in deterministic enterprise.
  arXiv  Detail & Related papers  (2026-05-10T06:29:34Z)
• A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications [18.01291032624805]
  Large language model (LLM)-based agents that reason, plan, and act through tools, memory, and structured interaction are emerging as a promising paradigm for automating complex tasks.<n>Yet as agents move toward open-ended, real-world deployment, relying on from-scratch reasoning and low-level tool calls for every task become increasingly inefficient, error-prone, and hard to maintain.<n>This survey examines this challenge through the lens of emphagent skills, which we define as reusable procedural artifacts that coordinate tools, memory, and runtime context under task-specific constraints.
  arXiv  Detail & Related papers  (2026-05-08T07:10:26Z)
• The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration [58.61149924275458]
  Tool use enables large language models to access external information, invoke software systems, and act in digital environments beyond what can be solved from model parameters alone.<n>As agent systems evolve, the central problem has shifted from isolated invocation to multi-tool orchestration over long trajectories with intermediate state, execution feedback, changing environments, and practical constraints such as safety, cost, and verifiability.<n>We organize the literature around six core dimensions: inference-time planning and execution, training and trajectory construction, safety and control, efficiency under resource constraints, capability completeness in open environments, and benchmark design and evaluation.
  arXiv  Detail & Related papers  (2026-03-24T07:05:05Z)
• Agentic Reasoning for Large Language Models [122.81018455095999]
  Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making.<n>Large language models (LLMs) demonstrate strong reasoning capabilities in closed-world settings, but struggle in open-ended and dynamic environments.<n>Agentic reasoning marks a paradigm shift by reframing LLMs as autonomous agents that plan, act, and learn through continual interaction.
  arXiv  Detail & Related papers  (2026-01-18T18:58:23Z)
• AI Agent Systems: Architectures, Applications, and Evaluation [4.967019713320407]
  AI agents combine foundation models with reasoning, planning, memory, and tool use.<n>We organize prior work into a unified taxonomy spanning agent components.<n>We discuss key design trade-offs -- latency vs. accuracy, autonomy vs. controllability, and capability vs. reliability.
  arXiv  Detail & Related papers  (2026-01-05T02:38:40Z)
• Architectures for Building Agentic AI [0.0]
  This chapter argues that the reliability of agentic and generative AI is chiefly an architectural property.<n>Building on classical foundations, we propose a practical taxonomy-tool-using agents, memory-augmented agents, planning and self-improvement agents, multi-agent systems, and embodied or web agents.
  arXiv  Detail & Related papers  (2025-12-10T09:28:40Z)
• State and Memory is All You Need for Robust and Reliable AI Agents [29.259008600842517]
  Large language models (LLMs) have enabled powerful advances in natural language understanding and generation.<n>Yet their application to complex, real-world scientific remain limited by challenges in memory, planning, and tool integration.<n>Here, we introduce SciBORG, a modular agentic framework that allows LLM-based agents to autonomously plan, reason, and achieve robust and reliable domain-specific task execution.
  arXiv  Detail & Related papers  (2025-06-30T02:02:35Z)
• Flex: End-to-End Text-Instructed Visual Navigation from Foundation Model Features [59.892436892964376]
  We investigate the minimal data requirements and architectural adaptations necessary to achieve robust closed-loop performance with vision-based control policies.<n>Our findings are synthesized in Flex (Fly lexically), a framework that uses pre-trained Vision Language Models (VLMs) as frozen patch-wise feature extractors.<n>We demonstrate the effectiveness of this approach on a quadrotor fly-to-target task, where agents trained via behavior cloning successfully generalize to real-world scenes.
  arXiv  Detail & Related papers  (2024-10-16T19:59:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.