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：工业料箱拣选易缠绕物体时，机器人需在料箱中无孤立物体时执行拾取未缠绕物体或进行分离操作。机器人必须能基于当前观测灵活执行适当动作，这面临高遮挡杂乱环境、难以捉摸的缠绕现象及熟练操作规划的挑战。本文提出一种自主、高效且通用的方法用于工业料箱中易缠绕物体的拾取。首先，我们学习PickNet——一种将视觉观测映射为拾取孤立物体或分离缠绕物体像素级可能性并推断对应抓取的网络。然后，提出两种有效分离策略：将缠绕物投掷至缓冲箱以降低缠绕程度；通过PullNet（一种从视觉输入预测拉拽位置和方向的网络）规划在缓冲箱中拉拽分离缠绕物体。为高效收集训练PickNet和PullNet的数据，我们采用物理模拟器中算法监督的自我监督学习范式。真实实验表明，我们的策略可灵巧拾取易缠绕物体，成功率达90%。我们进一步通过拾取未见物体集验证策略的泛化性。补充材料、代码和视频见https://xinyiz0931.github.io/tangle。