Towards Agentic Robots: Model, Data, Environment, and Harness
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
| Authors | Chonghan Qin, Dongming Wu, Kaituo Feng et al. |
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| Published | 14 October 2026 |
| Last updated at source | — |
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