论文标题
通过奇异和抓紧方法进行自我监督的互动对象细分
Self-Supervised Interactive Object Segmentation Through a Singulation-and-Grasping Approach
论文作者
论文摘要
在非结构化环境中，使用看不见的对象进行实例分割是一个具有挑战性的问题。为了解决这个问题，我们提出了一种机器人学习方法，以积极与新物体进行互动，并收集每个对象的训练标签，以进一步进行微调以提高细分模型的性能，同时避免手动标记数据集的耗时过程。通过端到端的强化学习对奇异和抓斗（SAG）政策进行培训。考虑到一堆混乱的对象，我们的方法选择推动和抓住动作来打破混乱并进行对象不合时宜的抓握，而SAG策略则将视觉观测和不完美的分段作为输入。我们将问题分解为三个子任务：（1）对象奇异子任务旨在将对象彼此分开，这会产生更多的空间，从而减轻了（2）无碰撞抓握子任务的难度； （3）通过使用基于光流的二进制分类器和运动提示后处理进行转移学习，掩盖了子任务以获得自标记的地面真相掩蔽。我们的系统在模拟的混乱场景中达到了70％的单次成功率。我们系统的交互式分割可在玩具块，仿真和现实世界中的YCB对象的平均精度和现实世界中的YCB对象平均精度达到87.8％，73.9％和69.3％，这表现优于几个基准。
Instance segmentation with unseen objects is a challenging problem in unstructured environments. To solve this problem, we propose a robot learning approach to actively interact with novel objects and collect each object's training label for further fine-tuning to improve the segmentation model performance, while avoiding the time-consuming process of manually labeling a dataset. The Singulation-and-Grasping (SaG) policy is trained through end-to-end reinforcement learning. Given a cluttered pile of objects, our approach chooses pushing and grasping motions to break the clutter and conducts object-agnostic grasping for which the SaG policy takes as input the visual observations and imperfect segmentation. We decompose the problem into three subtasks: (1) the object singulation subtask aims to separate the objects from each other, which creates more space that alleviates the difficulty of (2) the collision-free grasping subtask; (3) the mask generation subtask to obtain the self-labeled ground truth masks by using an optical flow-based binary classifier and motion cue post-processing for transfer learning. Our system achieves 70% singulation success rate in simulated cluttered scenes. The interactive segmentation of our system achieves 87.8%, 73.9%, and 69.3% average precision for toy blocks, YCB objects in simulation and real-world novel objects, respectively, which outperforms several baselines.
