DALL-E-Bot: 将网络规模扩散模型引入机器人学
DALL-E-Bot: Introducing Web-Scale Diffusion Models to Robotics
AI总结：
该研究提出DALL-E-Bot，首次将网络规模扩散模型DALL-E用于机器人场景物体 rearrangement，通过文本推断、目标图生成、实物摆放实现零样本自主操作，验证了该技术路线对可扩展无监督机器人学习的价值。
摘要：
我们提出了首个探索机器人学领域网络规模扩散模型的研究。DALL-E-Bot可让机器人 rearrange 场景中的物体：它首先推断这些物体的文本描述，接着生成一张呈现符合人类习惯的自然物体摆放效果的图像，最后根据该目标图像实际摆放物体。我们证明，借助DALL-E可实现零样本完成该任务，无需额外的摆放示例、数据收集或训练。得益于DALL-E的网络规模预训练，DALL-E-Bot完全自主，且不受限于预定义的物体或场景集合。人类研究与客观指标均得出了令人鼓舞的真实世界结果，表明将网络规模扩散模型整合到机器人流水线中，是实现可扩展无监督机器人学习的极具前景的方向。
英文摘要：
We introduce the first work to explore web-scale diffusion models for robotics. DALL-E-Bot enables a robot to rearrange objects in a scene, by first inferring a text description of those objects, then generating an image representing a natural, human-like arrangement of those objects, and finally physically arranging the objects according to that goal image. We show that this is possible zero-shot using DALL-E, without needing any further example arrangements, data collection, or training. DALL-E-Bot is fully autonomous and is not restricted to a pre-defined set of objects or scenes, thanks to DALL-E's web-scale pre-training. Encouraging real-world results, with both human studies and objective metrics, show that integrating web-scale diffusion models into robotics pipelines is a promising direction for scalable, unsupervised robot learning.
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。
