SHRIMP：机器人任务计划的迭代优化
SHRIMP: Iterative Refinement of Robot Task Plans
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中文总结 AI 辅助
针对协作机器人任务计划的语义歧义与透明度不足问题，提出SHRIMP系统，可通过自然语言生成分层机器人原语计划并迭代修正，经35人用户研究验证其能提升用户感知控制与机器人透明度。
中文摘要 AI 辅助
随着协作机器人进入制造业、农业和医疗保健等领域，对机器人行为进行编程或调整通常需要大多数终端用户所缺乏的机器人专业知识，自然语言降低了这一障碍。大型语言模型（LLM）的最新进展使得将自然语言转换为机器人任务计划成为可能。然而，基于语言的任务规范存在语义歧义，生成式模型在语言指令如何转化为机器人动作方面缺乏透明度，导致用户在执行前难以验证计划。为解决这些问题，我们提出了SHRIMP系统，该系统允许用户使用自然语言自动生成分层机器人原语计划，并通过重新提示和显式纠正迭代修改计划。每次修改时，SHRIMP允许用户在仿真中验证计划，满意后再在物理机器人上执行。通过涉及35名参与者规划桌面厨房任务的用户研究，我们验证了SHRIMP可提升感知控制并增强机器人透明度。系统视频和源代码可在指定网址获取。
英文摘要
As collaborative robots have entered domains such as manufacturing, agriculture, and healthcare, programming or adapting robot behavior typically requires robotic expertise that most end users lack. Natural language lowers this barrier. Recent advancements in large language models (LLMs) have made it feasible to translate natural language into robot task plans. However, language-based task specification suffers from semantic ambiguity, and generative models lack transparency for how language instructions become robot actions, making it difficult for users to validate the plan before execution. To address these issues, we introduce SHRIMP, a system that allows users to automatically generate a hierarchical robot primitive plan using natural language and iteratively revise their plan through re-prompting and explicit correction. At each revision, SHRIMP allows users to validate their plan in simulation, and once satisfied, execute it on the physical robot. Through a user study involving participants planning tabletop kitchen tasks (n=35), we validate that SHRIMP improves perceived control and enhances robot transparency. System videos and source code are available at https://wisc-hci.github.io/SHRIMP.
发表机构
- University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
机构由 AI 辅助整理，请以论文原文为准。
