论文标题
用于机器人操作的多对象关键的自我监督学习
Self-Supervised Learning of Multi-Object Keypoints for Robotic Manipulation
论文作者
论文摘要
近年来，使用强化或模仿的政策学习方法取得了重大进展。但是，这两种技术仍然都遭受计算昂贵的损失，并且需要大量的培训数据。在现实世界的机器人操纵任务中，这个问题尤其普遍，在这种任务中，无法获得地面真相场景特征，而是从原始摄像头观察中学到的策略。在本文中，我们通过密集的对应借口任务来证明学习图像关键的功效，以实现下游策略学习。将先前的工作扩展到挑战多物体场景，我们表明我们的模型可以接受培训，以应对代表学习的重要问题，主要是规模不变和遮挡。我们评估了我们对不同机器人操纵任务的方法，将其与其他视觉表示学习方法进行比较，并证明其灵活性和有效性在样本效率的政策学习中。
In recent years, policy learning methods using either reinforcement or imitation have made significant progress. However, both techniques still suffer from being computationally expensive and requiring large amounts of training data. This problem is especially prevalent in real-world robotic manipulation tasks, where access to ground truth scene features is not available and policies are instead learned from raw camera observations. In this paper, we demonstrate the efficacy of learning image keypoints via the Dense Correspondence pretext task for downstream policy learning. Extending prior work to challenging multi-object scenes, we show that our model can be trained to deal with important problems in representation learning, primarily scale-invariance and occlusion. We evaluate our approach on diverse robot manipulation tasks, compare it to other visual representation learning approaches, and demonstrate its flexibility and effectiveness for sample-efficient policy learning.
