aiqbench analysis
RoboPilot advances robotic manipulation by integrating Chain-of-Thought reasoning into a closed-loop agentic framework that dynamically balances efficiency and accuracy through dual-thinking modes. The system demonstrates significant robustness in dynamic environments, outperforming baselines by 11% while successfully handling execution errors via real-time replanning. This work represents a critical step toward generalizable AI agents capable of executing complex, long-horizon tasks in unstructured physical settings.
RoboPilot: Generalizable Dynamic Robotic Manipulation with Dual-thinking Modes
arXiv:2510.00154v2 Announce Type: replace-cross Abstract: Despite rapid progress in robotics, complex or long-horizon tasks remain a fundamental challenge. Most current approaches follow an open-loop paradigm with limited reasoning and no feedback, resulting in poor robustness to environmental changes and severe error accumulation. We present RoboPilot, a dual-thinking closed-loop agentic framework for robotic manipulation that supports adaptive reasoning for complex tasks in real-world dynamic environments. RoboPilot leverages primitive actions for structured task planning and flexible action generation as a agentic system, while introducing feedback to enable replanning from dynamic changes and execution errors. Chain-of-Thought reasoning further enhances high-level task planning and guides low-level action generation. The agentic system dynamically switches between fast and slow thinking to balance efficiency and accuracy. To systematically evaluate the robustness of RoboPilot in diverse robot manipulation scenarios, we introduce RoboPilot-Bench, a benchmark spanning 21 tasks across 10 categories, including infeasible-task recognition and dynamic recovery. Experiments show that RoboPilot outperforms state-of-the-art baselines by 11% in task success rate, and the real-world deployment on an industrial robot further demonstrates its robustness.
Abstract:Despite rapid progress in robotics, complex or long-horizon tasks remain a fundamental challenge. Most current approaches follow an open-loop paradigm with limited reasoning and no feedback, resulting in poor robustness to environmental changes and severe error accumulation. We present RoboPilot, a dual-thinking closed-loop agentic framework for robotic manipulation that supports adaptive reasoning for complex tasks in real-world dynamic environments. RoboPilot leverages primitive actions for structured task planning and flexible action generation as a agentic system, while introducing feedback to enable replanning from dynamic changes and execution errors. Chain-of-Thought reasoning further enhances high-level task planning and guides low-level action generation. The agentic system dynamically switches between fast and slow thinking to balance efficiency and accuracy. To systematically evaluate the robustness of RoboPilot in diverse robot manipulation scenarios, we introduce RoboPilot-Bench, a benchmark spanning 21 tasks across 10 categories, including infeasible-task recognition and dynamic recovery. Experiments show that RoboPilot outperforms state-of-the-art baselines by 11% in task success rate, and the real-world deployment on an industrial robot further demonstrates its robustness.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2510.00154 [cs.RO]
(or arXiv:2510.00154v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2510.00154 Focus to learn more arXiv-issued DOI via DataCite
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