We consider the problem of sorting a densely cluttered pile of unknown objects using a robot. This yet unsolved problem is relevant in the robotic waste sorting business.
By extending previous active learning approaches to grasping, we show a system that learns the task autonomously. Instead of predicting just whether a grasp succeeds, we predict the classes of the objects that end up being picked and thrown onto the target conveyor. Segmenting and identifying objects from the uncluttered target conveyor, as opposed to the working area, is easier due to the added structure since the thrown objects will be the only ones present.
Instead of trying to segment or otherwise understand the cluttered working area in any way, we simply allow the controller to learn a mapping from an RGBD image in the neighborhood of the grasp to a predicted result---all segmentation etc. in the working area is implicit in the learned function. The grasp selection operates in two stages: The first stage is hardcoded and outputs a distribution of possible grasps that sometimes succeed. The second stage uses a purely learned criterion to choose the grasp to make from the proposal distribution created by the first stage.
In an experiment, the system quickly learned to make good pickups and predict correctly, in advance, which class of object it was going to pick up and was able to sort the objects from a densely cluttered pile by color.
First Lessons Learned of an Artificial Intelligence Robotic System for Autonomous Coarse Waste Recycling Using Multispectral Imaging-Based Methods
Hamburg University of Applied Sciences（汉堡应用技术大学）；University of the West of Scotland（西苏格兰大学）；Institute for Energy, Recycling and Environmental Protection at Bremen University of Applied Sciences（不来梅应用技术大学能源、回收与环境保护研究所）；German Research Center for Artificial Intelligence（德国人工智能研究中心）
Category-level Shape Estimation for Densely Cluttered Objects
Beijing University of Posts and Telecommunications（北京邮电大学）；Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）
Digital Twin Driven Textile Classification and Foreign Object Recognition in Automated Sorting Systems
Institute of Smart Systems Technologies（智能系统技术研究所）；University of Klagenfurt（克雷格弗特大学）；AAU SAL USE Laboratory（AAU SAL USE实验室）；Silicon Austria Labs（硅 Austria 实验室）
