Metal recycling in scrapyards, where workers cut decommissioned structures using gas torches, is labor-intensive, difficult, and dangerous. As global metal scrap recycling demands are rising, robotics and automation technologies could play a significant role to address this demand. However, the unstructured nature of the scrap cutting problem—due to highly variable object shapes and environments—poses significant challenges to integrate robotic solutions. We propose a novel collaborative workflow for robotic metal cutting that combines worker expertise with robot autonomy. In this workflow, the skilled worker studies the scene, determines an appropriate cutting reference, and marks it on the object with spray paint. The robot, then, autonomously explores the surface of the object for identifying and reconstructing the drawn reference, converts it to a cutting trajectory, and finally executes the cut. This paper focuses on the surface exploration and cutting reference reconstruction tasks, which require appropriate next view planning (NVP) algorithms. We devise three NVP algorithms enabling the robot to explore and extract desired features from the scene,i.e., the drawn reference, without requiring anya prioriobject model. Contrasting with global or feature-agnostic NVP algorithms, our approaches guide the robot via desired local features to increase the efficiency of the exploration. We evaluate our NVP algorithms against six categories of objects both in simulation and in physical experiments.Note to Practitioners—This work is motivated by the need of extracting a desired cutting reference determined and drawn on the object by scrap yard workers. From the robot’s perspective, it must explore and reconstruct the drawing, starting from an unknown scene containing an unknown object featuring an unknown drawing. We assume that an RGB-D camera is attached to the tool-tip of the robot, and the color of the drawn path is significantly different from the object’s color. The goal of the robotic system is to explore the object surface to uncover the drawn path entirely without colliding with the object. The exploration algorithm must overcome complex object shapes and must be fast enough for practical use in scrap yards. This means conventional exploration (active vision) techniques are insufficient since they focus on exploring the entirety of the object, which is unnecessary for our task and is time-consuming. Our methods exploit the drawing information to guide the exploration for quickly determining a suitable viewpoint, which results in an efficient extraction of the entire cutting reference, without needing to explore the entire object’s surface. Our algorithms are robust against adversarial features such as discontinuous, non-smooth, or self-occluded object surfaces. Our feature-driven strategies are not limited to robotic scrap cutting as they are applicable to any viewpoint planning problem requiring high performance while extracting the local features in the scene.

Less than 35% of recyclable waste is being actually recycled in the US [2], which leads to increased soil and sea pollution and is one of the major concerns of environmental researchers as well as the common public. At the heart of the problem are the inefficiencies of the waste sorting process (separating paper, plastic, metal, glass, etc.) due to the extremely complex and cluttered nature of the waste stream. Recyclable waste detection poses a unique computer vision challenge as it requires detection of highly deformable and often translucent objects in cluttered scenes without the kind of context information usually present in human-centric datasets. This challenging computer vision task currently lacks suitable datasets or methods in the available literature. In this paper, we take a step towards computer-aided waste detection and present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste. We believe that ZeroWaste will catalyze research in object detection and semantic segmentation in extreme clutter as well as applications in the recycling domain. Our project page can be found at http://ai.bu.edu/zerowaste/

We present an ensemble learning methodology that combines multiple existing robotic grasp synthesis algorithms and obtain a success rate that is significantly better than the individual algorithms. The methodology treats the grasping algorithms as "experts" providing grasp "opinions". An Ensemble Convolutional Neural Network (ECNN) is trained using a Mixture of Experts (MOE) model that integrates these opinions and determines the final grasping decision. The ECNN introduces minimal computational cost overhead, and the network can virtually run as fast as the slowest expert. We test this architecture using open-source algorithms in the literature by adopting GQCNN 4.0, GGCNN and a custom variation of GGCNN as experts and obtained a 6% increase in the grasp success on the Cornell Dataset compared to the best-performing individual algorithm. The performance of the method is also demonstrated using a Franka Emika Panda arm.