机器人通过真实世界经验学习任务规划，成功率提升近三倍。
A Pragmatist Robot: Learning to Plan Tasks by Experiencing the Real World
- 用视觉语言模型模拟机器人感官与思考，实时反思失败动作
- 短期记忆让机器人任务中快速调整，长期记忆支持跨任务迁移
- 适合希望提升机器人自主学习能力的研究者和工程师
大型语言模型（LLMs）已成为基于自然语言指令进行机器人任务规划的主流方法。然而，由于训练数据来自互联网，其行为与真实机器人的具身性、技能范围和局限性不一致。受口头强化学习启发——即无需参数更新即可通过自我反思和少量示例学习——我们提出普拉格马特（PragmaBot）框架，使机器人能够通过真实世界经验学习任务规划。PragmaBot采用视觉语言模型（VLM）作为机器人的“大脑”与“眼睛”，使其能视觉评估动作结果并自我反思失败。这些反思存储在短期记忆（STM）中，实现任务中的快速行为调整。任务完成后，机器人将经验总结至长期记忆（LTM）。面对新任务时，可借助检索增强生成（RAG）调用相关过往经验，制定更贴近现实的动作序列。四组挑战性任务实验表明，基于STM的自我反思使任务成功率从35%提升至84%，并涌现出智能物体交互行为。在12个真实场景（含8个未见过的任务）中，机器人通过LTM学习，单次尝试成功率从22%提升至80%，且RAG优于直接提示。结果验证了PragmaBot的有效性与泛化能力。
原文摘要 · Abstract (English)
Large language models (LLMs) have emerged as the dominant paradigm for robotic task planning using natural language instructions. However, trained on general internet data, LLMs are not inherently aligned with the embodiment, skill sets, and limitations of real-world robotic systems. Inspired by the emerging paradigm of verbal reinforcement learning-where LLM agents improve through self-reflection and few-shot learning without parameter updates-we introduce PragmaBot, a framework that enables robots to learn task planning through real-world experience. PragmaBot employs a vision-language model (VLM) as the robot's "brain" and "eye", allowing it to visually evaluate action outcomes and self-reflect on failures. These reflections are stored in a short-term memory (STM), enabling the robot to quickly adapt its behavior during ongoing tasks. Upon task completion, the robot summarizes the lessons learned into its long-term memory (LTM). When facing new tasks, it can leverage retrieval-augmented generation (RAG) to plan more grounded action sequences by drawing on relevant past experiences and knowledge. Experiments on four challenging robotic tasks show that STM-based self-reflection increases task success rates from 35% to 84%, with emergent intelligent object interactions. In 12 real-world scenarios (including eight previously unseen tasks), the robot effectively learns from the LTM and improves single-trial success rates from 22% to 80%, with RAG outperforming naive prompting. These results highlight the effectiveness and generalizability of PragmaBot. Project webpage: https://pragmabot.github.io/
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