让机器人从一次示范中学习，自动调整目标以更轻松完成任务。
Adaptation of Task Goal States from Prior Knowledge
- 基于单次任务示范生成环境变化模型
- 可自适应调整目标状态，提升执行成功率
- 适合需要灵活适应的机器人任务场景
本文提出一种框架，用于在任务目标状态上引入自由度与多样性。机器人可通过观察任务执行过程，选择一个与任务描述兼容但对自身更易实现的新目标。文中定义了环境状态与环境变化的模型，并展示了如何从单次任务示范中交互式生成变化模式，以及如何利用该变化生成将任意环境状态引导至目标状态的执行计划。
原文摘要 · Abstract (English)
This paper presents a framework to define a task with freedom and variability in its goal state. A robot could use this to observe the execution of a task and target a different goal from the observed one; a goal that is still compatible with the task description but would be easier for the robot to execute. We define the model of an environment state and an environment variation, and present experiments on how to interactively create the variation from a single task demonstration and how to use this variation to create an execution plan for bringing any environment into the goal state.
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