RoboPilot让机器人动态任务执行更可靠，支持实时纠错与自适应规划。
RoboPilot: Generalizable Dynamic Robotic Manipulation with Dual-thinking Modes
- 双思维模式：快思（高效）与慢思（精准）切换，兼顾速度与准确率。
- 在21项任务上成功率达93.4%，比现有方法提升25.9%。
- 适合工业机器人、复杂场景下需自主决策的智能系统研究者。
尽管自主机器人技术快速发展，但执行复杂或长时程任务仍是核心挑战。现有方法多采用开环范式，缺乏推理与反馈机制，导致对环境变化鲁棒性差且误差累积严重。本文提出RoboPilot，一种支持复杂任务自适应推理的双思维闭环框架，通过基础动作实现结构化任务规划与灵活动作生成，并引入反馈机制以应对环境变化和执行错误。链式思维推理进一步增强高层任务规划并指导底层动作生成。系统可动态切换快速与慢速思维模式，平衡效率与精度。为系统评估鲁棒性，我们构建了涵盖10大类共21项任务的RoboPilot-Bench基准，包括不可行任务识别与故障恢复能力测试。实验表明，RoboPilot在任务成功率上优于当前最优基线25.9%，在工业机器人上的真实部署也验证了其在现实场景中的强鲁棒性。
原文摘要 · Abstract (English)
Despite rapid progress in autonomous robotics, executing complex or long-horizon tasks remains 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 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, 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 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 failure recovery. Experiments show that RoboPilot outperforms state-of-the-art baselines by 25.9\% in task success rate, and the real-world deployment on an industrial robot further demonstrates its robustness in real-world settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。