用自然语言生成机器人任务计划并支持迭代修正，提升用户控制感与透明度。
SHRIMP: Iterative Refinement of Robot Task Plans
- 通过自然语言生成层级化机器人动作计划，支持用户逐步优化
- 用户可在仿真中验证计划，最终在真实机器人上执行
- 适用于非专业用户在厨房等场景规划机器人任务
随着协作机器人进入制造、农业和医疗等领域，编程或调整机器人行为通常需要机器人专业知识，而大多数终端用户缺乏此类技能。自然语言可降低这一门槛。近年来大语言模型（LLMs）的发展使将自然语言转化为机器人任务计划成为可能。然而，基于语言的任务描述存在语义模糊性，生成模型在语言指令如何转化为机器人动作方面缺乏透明度，导致用户难以在执行前验证计划。为此，我们提出SHRIMP系统，允许用户通过自然语言自动生成分层机器人基础动作计划，并通过重新提示和显式修正进行迭代修订。每次修订后，用户可在仿真环境中验证计划，满意后在真实机器人上执行。一项涉及35名参与者规划桌面厨房任务的用户研究验证了SHRIMP能提升用户感知控制力并增强机器人透明度。系统视频与源代码见https://wisc-hci.github.io/SHRIMP。
原文摘要 · Abstract (English)
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.
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