面向工业料箱拣选的易缠绕物体灵巧抓取或分离学习
Learning to Dexterously Pick or Separate Tangled-Prone Objects for Industrial Bin Picking
AI总结：
本文提出 PickNet 与 PullNet 结合的自监督学习方法，通过抓取、丢入缓冲箱和拉动分离策略，实现工业料箱中易缠绕物体的灵巧拣选，成功率达90%。
摘要：
工业料箱拣选中，对于易缠绕物体，机器人需要要么抓取未缠绕的物体，要么在料箱中没有孤立物体时执行分离操作。机器人必须能够根据当前观测灵活地执行合适的动作。由于杂乱场景中的高度遮挡、难以捉摸的缠绕现象以及熟练操作规划的需求，这一任务极具挑战性。本文提出了一种自主、有效且通用的工业料箱拣选易缠绕物体的方法。首先，我们学习 PickNet——一个将视觉观测映射到像素级可能性的网络，用于判断抓取孤立物体或分离缠绕物体，并推断相应的抓取。然后，我们提出两种有效的分离策略：将缠绕物体丢入缓冲料箱以降低缠绕程度；通过 PullNet 规划的拉动操作来分离缓冲料箱中的缠绕物体——PullNet 是一个从视觉输入预测拉动位置和方向的网络。为了高效收集训练 PickNet 和 PullNet 的数据，我们采用自监督学习范式，在物理仿真器中使用算法监督器。真实世界实验表明，我们的策略能够灵巧地抓取易缠绕物体，成功率达到 90%。我们进一步通过抓取一组未见过的物体来证明该策略的泛化能力。补充材料、代码和视频可在 https://xinyiz0931.github.io/tangle 找到。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
