用大模型分步规划，零样本实现机器人精准执行指令
FlowPlan: Zero-Shot Task Planning with LLM Flow Engineering for Robotic Instruction Following
- 将任务规划拆解为检索、推理、符号规划和逻辑验证四阶段
- 在ALFRED数据集上达到与有监督方法相当的零样本性能
- 适合无标注数据的机器人指令执行场景
机器人指令执行任务需要融合视觉感知、任务规划、目标定位和运动执行。现有方法在零样本场景下表现不佳，因难以将长指令准确转化为可执行计划。为此，我们提出FlowPlan，一种结构化的多阶段大模型工作流，显著提升零样本规划能力，缩小与有监督上下文学习方法的差距。通过分解为任务信息检索、语言级推理、符号级规划和逻辑评估四个模块，FlowPlan生成符合操作约束的逻辑连贯动作序列，并提取上下文信息用于精确的目标定位。在ALFRED数据集上验证，性能接近数据驱动的上下文学习方法，并在真实环境中展示跨环境适应性。该工作实现了无需标注数据的零样本任务规划。
原文摘要 · Abstract (English)
Robotic instruction following tasks require seamless integration of visual perception, task planning, target localization, and motion execution. However, existing task planning methods for instruction following are either data-driven or underperform in zero-shot scenarios due to difficulties in grounding lengthy instructions into actionable plans under operational constraints. To address this, we propose FlowPlan, a structured multi-stage LLM workflow that elevates zero-shot pipeline and bridges the performance gap between zero-shot and data-driven in-context learning methods. By decomposing the planning process into modular stages--task information retrieval, language-level reasoning, symbolic-level planning, and logical evaluation--FlowPlan generates logically coherent action sequences while adhering to operational constraints and further extracts contextual guidance for precise instance-level target localization. Benchmarked on the ALFRED and validated in real-world applications, our method achieves competitive performance relative to data-driven in-context learning methods and demonstrates adaptability across diverse environments. This work advances zero-shot task planning in robotic systems without reliance on labeled data. Project website: https://instruction-following-project.github.io/.
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。