While large-scale robotic systems typically rely on textual instructions for tasks, this work explores a different approach: can robots infer the task directly from observing humans? This shift necessitates the robot's ability to decode human intent and translate it into executable actions within its physical constraints and environment. We introduce Vid2Robot, a novel end-to-end video-based learning framework for robots. Given a video demonstration of a manipulation task and current visual observations, Vid2Robot directly produces robot actions. This is achieved through a unified representation model trained on a large dataset of human video and robot trajectory. The model leverages cross-attention mechanisms to fuse prompt video features to the robot's current state and generate appropriate actions that mimic the observed task. To further improve policy performance, we propose auxiliary contrastive losses that enhance the alignment between human and robot video representations. We evaluate Vid2Robot on real-world robots, demonstrating a 20% improvement in performance compared to other video-conditioned policies when using human demonstration videos. Additionally, our model exhibits emergent capabilities, such as successfully transferring observed motions from one object to another, and long-horizon composition, thus showcasing its potential for real-world applications. Project website: vid2robot.github.io

通过观察人类行为并将其翻译成可执行的动作，本研究介绍了一种基于视频学习的机器人框架Vid2Robot，它通过训练机器人模型利用人类视频和机器人轨迹数据集进行任务执行。该模型利用交叉注意力机制将提示视频特征融合到机器人的当前状态中，并生成能够模仿所观察任务的适当动作，大幅提升执行效果，同时显示着在真实世界应用中的潜力。

Vid2Robot: 基于跨引注意力变形器的端到端视频条件策略学习