This paper addresses category-agnostic instance segmentation for robotic manipulation, focusing on segmenting objects independent of their class to enable versatile applications like bin-picking in dynamic environments. Existing methods often lack generalizability and object-specific information, leading to grasp failures. We present a novel approach leveraging object-centric instance segmentation and simulation-based training for effective transfer to real-world scenarios. Notably, our strategy overcomes challenges posed by noisy depth sensors, enhancing the reliability of learning. Our solution accommodates transparent and semi-transparent objects which are historically difficult for depth-based grasping methods. Contributions include domain randomization for successful transfer, our collected dataset for warehouse applications, and an integrated framework for efficient bin-picking. Our trained instance segmentation model achieves state-of-the-art performance over WISDOM public benchmark [1] and also over the custom-created dataset. In a real-world challenging bin-picking setup our bin-picking framework method achieves 98% accuracy for opaque objects and 97% accuracy for non-opaque objects, outperforming the state-of-the-art baselines with a greater margin.

翻译：本文针对机器人操作中的类别无关实例分割问题，重点研究独立于物体类别的分割方法，以实现在动态环境（如箱拣）中的通用应用。现有方法通常缺乏泛化能力与物体特定信息，导致抓取失败。我们提出一种新颖方法，利用以物体为中心的实例分割和基于仿真的训练，有效迁移至真实场景。值得注意的是，我们的策略克服了噪声深度传感器带来的挑战，增强了学习的可靠性。该方案能够处理传统基于深度抓取方法难以应对的透明与半透明物体。主要贡献包括：用于成功迁移的域随机化技术、针对仓储应用自建的数据集，以及一个集成化高效箱拣框架。训练的实例分割模型在WISDOM公开基准[1]及自定义数据集上均达到最优性能。在具有挑战性的真实箱拣场景中，我们的箱拣框架对不透明物体准确率达98%，对非透明物体达97%，大幅超越现有最优基线方法。