DeLTa: 人机交互与语言引导的新透明物体操控演示
DeLTa: Demonstration and Language-Guided Novel Transparent Object Manipulation
AI总结：
DeLTa通过整合深度估计、6D姿态估计和视觉-语言规划，实现基于自然语言指令的精确长周期透明物体操控，无需额外训练或类别先验。
摘要：
尽管透明物体交互在人类日常生活中非常普遍，但透明机器人操控研究仍局限于短周期任务和基本抓取能力。虽然一些方法部分解决了这些问题，但大多数方法在泛化到新物体时存在局限，并且不足以支持精确的长周期机器人操控。为了解决这一限制，我们提出了DeLTa（演示和语言引导的新透明物体操控），这是一个新的框架，整合了深度估计、6D姿态估计和视觉-语言规划，用于精确的长周期透明物体操控，该操控由自然语言任务指令引导。我们方法的一个关键优势是其单演示方法，该方法能够将6D轨迹泛化到新的透明物体，而无需要求类别级先验或额外训练。此外，我们还提出了一种任务规划器，该规划器细化VLM生成的计划，以考虑单臂、眼在手机器人的约束，以进行长周期物体操控任务。通过全面评估，我们证明我们的方法在现有透明物体操控方法上表现显著更好，特别是在需要精确操控能力的长周期场景中。项目页面：https://sites.google.com/view/DeLTa25/
英文摘要：
Despite the prevalence of transparent object interactions in human everyday life, transparent robotic manipulation research remains limited to short-horizon tasks and basic grasping capabilities. Although some methods have partially addressed these issues, most of them have limitations in generalization to novel objects and are insufficient for precise long-horizon robot manipulation. To address this limitation, we propose DeLTa (Demonstration and Language-Guided Novel Transparent Object Manipulation), a novel framework that integrates depth estimation, 6D pose estimation, and vision-language planning for precise long-horizon manipulation of transparent objects guided by natural language task instructions. A key advantage of our method is its single-demonstration approach, which generalizes 6D trajectories to novel transparent objects without requiring category-level priors or additional training. Additionally, we present a task planner that refines the VLM-generated plan to account for the constraints of a single-arm, eye-in-hand robot for long-horizon object manipulation tasks. Through comprehensive evaluation, we demonstrate that our method significantly outperforms existing transparent object manipulation approaches, particularly in long-horizon scenarios requiring precise manipulation capabilities. Project page: https://sites.google.com/view/DeLTa25/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
