In this work, we aim to learn a unified vision-based policy for a multi-fingered robot hand to manipulate different objects in diverse poses. Though prior work has demonstrated that human videos can benefit policy learning, performance improvement has been limited by physically implausible trajectories extracted from videos. Moreover, reliance on privileged object information such as ground-truth object states further limits the applicability in realistic scenarios. To address these limitations, we propose a new framework ViViDex to improve vision-based policy learning from human videos. It first uses reinforcement learning with trajectory guided rewards to train state-based policies for each video, obtaining both visually natural and physically plausible trajectories from the video. We then rollout successful episodes from state-based policies and train a unified visual policy without using any privileged information. A coordinate transformation method is proposed to significantly boost the performance. We evaluate our method on three dexterous manipulation tasks and demonstrate a large improvement over state-of-the-art algorithms.

我们的研究旨在通过学习一种统一的基于视觉的策略，使多指机械手能够在各种姿势下操纵不同的物体。我们提出了一种新的框架 ViViDex，通过使用强化学习和轨迹引导奖励训练每个视频的基于状态的策略，从而获得视觉自然且物理可行的轨迹，并在不使用任何特权信息的情况下训练出一个统一的视觉策略，该方法通过坐标转换方法显著提升了性能，在三个灵巧操纵任务上展示出了较大的改进。

ViViDex: 从人类视频中学习基于视觉的灵巧操纵