外观
论文arXiv 2610.2044214 天前
迈向智能体式机器人：模型、数据、环境与 Harness
原文链接：Towards Agentic Robots: Model, Data, Environment, and Harness（arXiv 2610.20442 · 发布日期：2026-10-14）
Towards Agentic Robots: Model, Data, Environment, and Harness
本文针对智能体式机器人研究割裂的问题，提出统一综述框架，梳理现有研究与未来方向。
核心要点
- 动机是基础模型推动机器人智能从低层动作预测转向目标导向的系统编排。
- 论文界定智能体式机器人的定义与范围，并回顾从机器人策略到智能体系统的发展。
- 方法上将现有研究归入模型、数据、环境、Harness四个功能域下的十七类。
- 论文讨论开放挑战和未来方向，旨在帮助构建更强、更可靠、更自适应的机器人。
验证范围与局限：综述范围为现有研究，摘要未提及具体仿真或真机验证。
英文摘要 (Abstract)
Foundation models are driving a paradigm shift in robotic intelligence, moving beyond low-level action prediction to goal-directed orchestration of data collection, model training, task planning, and real-world execution. We characterize this emerging paradigm as an agentic robots, in which agents reason over human instructions, interact with robotic environments, rectify execution errors autonomously, and dynamically adjust operational strategies. Although this field develops fast, current studies are not well connected. Researchers have no consistent view of how to combine different agentic functions into a complete robot system. This paper provides a unified overview of existing research on agentic robots. We first trace the development from robot policies to agentic systems, covering vision-language-action models, world models, and world-action models. We then clarify the definition and scope of agentic robots and organize existing research into seventeen categories across four functional domains: Model, Data, Environment, and Harness. Across these domains, we examine how agents shape model learning and robot decisions, collect and curate robot data, construct worlds, tasks, and tests for learning and evaluation, and organize capabilities for robot use. Finally, we discuss open challenges and outline directions for future research. Looking forward, we hope this work contributes to a clearer understanding of agentic robots and supports new approaches to making them more capable, reliable, and adaptive.
作者：Chonghan Qin, Dongming Wu, Kaituo Feng, Jiaxu Wang, Xinlei Yu, Peiwen Sun, Yijia Fan, Xilin He, Bingao Chen, Weitong Lian
来源
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