Grasping Partially Occluded Objects Using Autoencoder-Based Point Cloud Inpainting

Flexible industrial production systems will play a central role in the future of manufacturing due to higher product individualization and customization. A key component in such systems is the robotic grasping of known or unknown objects in random positions. Real-world applications often come with challenges that might not be considered in grasping solutions tested in simulation or lab settings. Partial occlusion of the target object is the most prominent. Examples of occlusion can be supporting structures in the camera's field of view, sensor imprecision, or parts occluding each other due to the production process. In all these cases, the resulting lack of information leads to shortcomings in calculating grasping points. In this paper, we present an algorithm to reconstruct the missing information. Our inpainting solution facilitates the real-world utilization of robust object matching approaches for grasping point calculation. We demonstrate the benefit of our solution by enabling an existing grasping system embedded in a real-world industrial application to handle occlusions in the input. With our solution, we drastically decrease the number of objects discarded by the process.

The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Research

In this paper, we investigate the practical relevance of explainable artificial intelligence (XAI) with a special focus on the producing industries and relate them to the current state of academic XAI research. Our findings are based on an extensive series of interviews regarding the role and applicability of XAI along the Machine Learning (ML) lifecycle in current industrial practice and its expected relevance in the future. The interviews were conducted among a great variety of roles and key stakeholders from different industry sectors. On top of that, we outline the state of XAI research by providing a concise review of the relevant literature. This enables us to provide an encompassing overview covering the opinions of the surveyed persons as well as the current state of academic research. By comparing our interview results with the current research approaches we reveal several discrepancies. While a multitude of different XAI approaches exists, most of them are centered around the model evaluation phase and data scientists. Their versatile capabilities for other stages are currently either not sufficiently explored or not popular among practitioners. In line with existing work, our findings also confirm that more efforts are needed to enable also non-expert users' interpretation and understanding of opaque AI models with existing methods and frameworks.