Industrial bin picking for tangled-prone objects requires the robot to either pick up untangled objects or perform separation manipulation when the bin contains no isolated objects. The robot must be able to flexibly perform appropriate actions based on the current observation. It is challenging due to high occlusion in the clutter, elusive entanglement phenomena, and the need for skilled manipulation planning. In this paper, we propose an autonomous, effective and general approach for picking up tangled-prone objects for industrial bin picking. First, we learn PickNet - a network that maps the visual observation to pixel-wise possibilities of picking isolated objects or separating tangled objects and infers the corresponding grasp. Then, we propose two effective separation strategies: Dropping the entangled objects into a buffer bin to reduce the degree of entanglement; Pulling to separate the entangled objects in the buffer bin planned by PullNet - a network that predicts position and direction for pulling from visual input. To efficiently collect data for training PickNet and PullNet, we embrace the self-supervised learning paradigm using an algorithmic supervisor in a physics simulator. Real-world experiments show that our policy can dexterously pick up tangled-prone objects with success rates of 90%. We further demonstrate the generalization of our policy by picking a set of unseen objects. Supplementary material, code, and videos can be found at https://xinyiz0931.github.io/tangle.

翻译：工业料箱拾取易缠绕物体时，机器人需要拾取未缠绕的物体，或在料箱中无孤立物体时执行分离操作。机器人必须根据当前观察灵活执行合适的动作。由于杂乱场景中的高度遮挡、难以捉摸的缠绕现象以及精细操作规划的需求，这一任务极具挑战性。本文提出了一种自主、高效且通用的方法，用于工业料箱拾取易缠绕物体。首先，我们学习PickNet——这是一个将视觉观察映射为像素级拾取孤立物体或分离缠绕物体可能性，并推断相应抓取姿态的网络。随后，我们提出两种有效的分离策略：将缠绕物体抛入缓冲料箱以降低缠绕程度；通过PullNet规划在缓冲料箱中拉扯分离缠绕物体——PullNet是一种从视觉输入预测拉扯位置和方向的网络。为高效收集训练PickNet和PullNet的数据，我们在物理仿真器中采用自监督学习范式，利用算法监督器进行数据采集。真实世界实验表明，我们的策略能灵巧拾取易缠绕物体，成功率高达90%。我们进一步通过拾取一组未见物体验证了策略的泛化能力。补充材料、代码和视频可访问：https://xinyiz0931.github.io/tangle。