RoboPilot：具备双思维模式的可泛化动态机器人操作
RoboPilot: Generalizable Dynamic Robotic Manipulation with Dual-thinking Modes
AI总结：
针对现有机器人开环操作方法鲁棒性差、误差累积的问题，提出具备快慢双思维切换的闭环框架RoboPilot，结合基元动作、反馈重规划与思维链推理，在含21项任务的基准上任务成功率超SOTA基线25.9%，真实工业部署验证了鲁棒性。
摘要：
尽管自主机器人领域进展迅速，但执行复杂或长时域任务仍是一项核心挑战。当前大多数方法采用开环范式，推理能力有限且无反馈机制，导致对环境变化的鲁棒性差，误差累积严重。我们提出RoboPilot，一种面向机器人操作的双思维闭环框架，支持在真实动态环境中针对复杂任务进行自适应推理。RoboPilot利用基元动作开展结构化任务规划与灵活动作生成，同时引入反馈机制以根据动态变化和执行误差重新规划。思维链（Chain-of-Thought）推理进一步增强了高层任务规划能力，并指导低层动作生成。该系统在快、慢思维之间动态切换，以平衡效率与准确性。为系统评估RoboPilot在多样机器人操作场景中的鲁棒性，我们推出了RoboPilot-Bench基准，涵盖10个类别共21项任务，包括不可行任务识别与故障恢复。实验表明，RoboPilot的任务成功率比当前最优基线高出25.9%，在工业机器人上的真实部署进一步验证了其在真实场景中的鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
