VCA：面向目标模糊环境中的分割物体精确操控的视觉-点击-动作框架
VCA: Vision-Click-Action Framework for Precise Manipulation of Segmented Objects in Target Ambiguous Environments
AI总结：
VCA框架通过视觉点击交互替代文本指令，实现目标模糊环境中分割物体的精确操控，提升机器人操作的准确性和实用性。
摘要：
在视觉-语言-动作（VLA）模型中依赖语言引入了歧义、认知负担以及在存在多个视觉相似物体的环境中精确物体识别和顺序任务执行的困难。为了解决这些限制，我们提出了视觉-点击-动作（VCA）框架，该框架用预训练的分割模型替代冗长的文本指令，采用直接的点击式视觉交互。通过允许操作员在机器人2D摄像头视图中通过视觉选择明确指定目标物体，VCA减少了解释错误，降低了认知负荷，并为现实世界的机器人操控提供了实用且可扩展的替代语言驱动接口。实验结果验证了所提出的VCA框架能够有效实现指定目标物体的实例级操控。实验视频可在https://robrosinc.github.io/vca/上获取。
英文摘要：
The reliance on language in Vision-Language-Action (VLA) models introduces ambiguity, cognitive overhead, and difficulties in precise object identification and sequential task execution, particularly in environments with multiple visually similar objects. To address these limitations, we propose Vision-Click-Action (VCA), a framework that replaces verbose textual commands with direct, click-based visual interaction using pretrained segmentation models. By allowing operators to specify target objects clearly through visual selection in the robot's 2D camera view, VCA reduces interpretation errors, lowers cognitive load, and provides a practical and scalable alternative to language-driven interfaces for real-world robotic manipulation. Experimental results validate that the proposed VCA framework achieves effective instance-level manipulation of specified target objects. Experiment videos are available at https://robrosinc.github.io/vca/.
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。
