Advancements in Industry 4.0 demand sophisticated solutions for automatic robotic assembly sequence planning (RASP), capable of handling the diversity and complexity of modern manufacturing tasks. One approach to RASP is Assembly-by-Disassembly (AbD). It first searches for a disassembly sequence that is then inverted to obtain an assembly sequence. One of the challenges of AbD, however, is the exponential number of potential assembly sequences for any given assembly. To mitigate this challenge, we propose to transfer knowledge obtained during previous planning attempts. Specifically, we present an approach that combines Monte Carlo Tree Search (MCTS) with deep Q-learning to optimize the total length of robotic assembly paths. We use a graph-based representation of disassembly states in combination with a graph neural network to learn the Q-function. We further discuss a principled approach to generate 3D assemblies out of aluminium profiles that a single robot manipulator can assemble. With this approach, we generated two datasets consisting of 14 assemblies with 21 removable parts and 7 assemblies with 30 removable parts. Using leave-one-out cross-validation, we were able to demonstrate how our approach outperformed an unmodified MCTS. Moreover, we successfully transferred knowledge between datasets.

Industry 4.0 facilitates a high number of product variants, posing significant challenges for modern manufacturing. One of them is the automatic creation of assembly sequences. This can be achieved with the assembly-by-disassembly (AbD) approach, which is currently highly inefficient. We aim at speeding up AbD by leveraging deep learning. AbD relies on iteratively testing parts for removal, which makes the order in which parts are tested highly relevant for its run-time. We optimize this order by training a graph neural network (GNN) based on the shape of parts and the shape of local part connections. For each part, it predicts a removability probability. We use these probabilities to optimize the order in which parts are tested for removal. This reduces the number of parts tested by approximately 64%-90%, depending on the tested product. Further improvements are achieved by combining our approach with bookkeeping, another approach for speeding up AbD. Finally, we separately analyze the impact of the parts and their connections on the removability probabilities predicted by the GNN. We found that most of the important information regarding a part's removability can be derived from its connections alone.

On robotics computer vision tasks, generating and annotating large amounts of data from real-world for the use of deep learning-based approaches is often difficult or even impossible. A common strategy for solving this problem is to apply simulation-to-reality (sim2real) approaches with the help of simulated scenes. While the majority of current robotics vision sim2real work focuses on image data, we present an industrial application case that uses sim2real transfer learning for point cloud data. We provide insights on how to generate and process synthetic point cloud data in order to achieve better performance when the learned model is transferred to real-world data. The issue of imbalanced learning is investigated using multiple strategies. A novel patch-based attention network is proposed additionally to tackle this problem.