机器人场景克隆：通过视觉提示编辑推进零样本机器人场景适应在操作中
Robotic Scene Cloning:Advancing Zero-Shot Robotic Scene Adaptation in Manipulation via Visual Prompt Editing
AI总结：
机器人场景克隆通过视觉提示编辑提升零样本机器人场景适应能力，实现跨场景的高效操作性能
摘要：
现代机器人可以在良好训练的环境中执行广泛简单的任务，并适应多样的场景。然而，在现实用户场景中部署预训练的机器人模型仍然具有挑战性，因为它们的零样本能力有限，通常需要大量的现场数据收集。为了解决这个问题，我们提出了机器人场景克隆（RSC），一种新的方法，通过编辑现有的机器人操作轨迹来实现场景特定的适应。RSC通过利用视觉提示机制和精心调制的条件注入模块，实现准确且场景一致的样本生成。不仅转移纹理，而且对视觉提示作出适度的形状适应，RSC在各种物体类型上展示了可靠的任务性能。在各种模拟和现实环境中的实验表明，RSC显著增强了在目标环境中的策略泛化能力。
英文摘要：
Modern robots can perform a wide range of simple tasks and adapt to diverse scenarios in the well-trained environment. However, deploying pre-trained robot models in real-world user scenarios remains challenging due to their limited zero-shot capabilities, often necessitating extensive on-site data collection. To address this issue, we propose Robotic Scene Cloning (RSC), a novel method designed for scene-specific adaptation by editing existing robot operation trajectories. RSC achieves accurate and scene-consistent sample generation by leveraging a visual prompting mechanism and a carefully tuned condition injection module. Not only transferring textures but also performing moderate shape adaptations in response to the visual prompts, RSC demonstrates reliable task performance across a variety of object types. Experiments across various simulated and real-world environments demonstrate that RSC significantly enhances policy generalization in target environments.
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。
