16 accepted papers
Attributing model behavior to training data is an evolving research field. A common benchmark is data removal, which involves eliminating data instances with either low or high values, then assessing a model's performance trained on the modified dataset. Many existing studies leverage Shapley-based
The rapid evolution of retail robotics is set to transform in-store operations through advanced automation, spanning vision-based inventory tracking, order picking, packing, and restocking. Yet fine-grained product identification remains a bottleneck: assortments change, packaging evolves, and shelv…
Robots operating in everyday environments encounter a wide variety of previously unseen objects. Deep Learning methods simplify unknown object and scene segmentation by structuring inherent real-world complexities, improving visual scene understanding. However, they need vast amounts of labeled high
To this day, only a small number of industrial robots is used in assembly. One key reason for this is that specific contact situations require the introduction of force-control schemes. The parameters for those schemes are hard to select in practice, because they require in-depth expertise about the…
Surface treatment tasks such as grinding, sanding or polishing are a vital step of the value chain in many industries, but are notoriously challenging to automate. We present RoboGrind, an integrated system for the intuitive, interactive automation of surface treatment tasks with industrial robots.…
Robotic applications like automated order picking in warehouses or retail stores, or fetch and carry tasks in hospitals, care homes, or households rely on the capability of service robots to find and handle a specific type of object. These applications are challenging as the set of objects is very l…
Compared to point estimates calculated by standard neural networks, Bayesian neural networks (BNN) provide probability distributions over the output predictions and model parameters, i.e., the weights. Training the weight distribution of a BNN, however, is more involved due to the intractability of…
Recent advances in Reinforcement Learning (RL) have made significant contributions in past years by offering intelligent solutions to solve robotic tasks. However, most RL algorithms, especially the model-free RL, are plagued by low learning efficiency and safety problems. In this paper, we propose…
Increasingly volatile markets challenge companies and demand flexible production systems that can be quickly adapted to new conditions. Machine Learning has proven to show significant potential in supporting the human operator during the time-consuming and complex task of robot pro-gramming by ident…
In this paper, we present a Domain Randomization and a Domain Adaptation approach to transfer experience for entanglement detection and separation from simulation into a real-world bin-picking application. We investigate the influence of different randomization options in image processing and use a…
Single shot approaches have demonstrated tremendous success on various computer vision tasks. Finding good parameterizations for 6D object pose estimation remains an open challenge. In this work, we propose different novel parameterizations for the output of the neural network for single shot 6D obj…
This paper introduces a novel approach for the grasping and precise placement of various known rigid objects using multiple grippers within highly cluttered scenes. Using a single depth image of the scene, our method estimates multiple 6D object poses together with an object class, a pose distance f…
In this paper, we introduce a novel single shot approach for 6D object pose estimation of rigid objects based on depth images. For this purpose, a fully convolutional neural network is employed, where the 3D input data is spatially discretized and pose estimation is considered as a regression task t…
Safety in Human-Robot Collaboration (HRC) is a bottleneck to HRC-productivity in industry. With robots being the main source of hazards, safety engineers use over-emphasized safety measures, and carry out lengthy and expensive risk assessment processes on each HRC-layout reconfiguration. Recent adva…
In this paper, we introduce a novel learning-based approach for grasping known rigid objects in highly cluttered scenes and precisely placing them based on depth images. Our Placement Quality Network (PQ-Net) estimates the object pose and the quality for each automatically generated grasp pose for m…
In this paper, we introduce a new public dataset for 6D object pose estimation and instance segmentation for industrial bin-picking. The dataset comprises both synthetic and real-world scenes. For both, point clouds, depth images, and annotations comprising the 6D pose (position and orientation), a…