Heuristic-free Optimization of Force-Controlled Robot Search Strategies in Stochastic Environments

In both industrial and service domains, a central benefit of the use of robots is their ability to quickly and reliably execute repetitive tasks. However, even relatively simple peg-in-hole tasks are typically subject to stochastic variations, requiring search motions to find relevant features such as holes. While search improves robustness, it comes at the cost of increased runtime: More exhaustive search will maximize the probability of successfully executing a given task, but will significantly delay any downstream tasks. This trade-off is typically resolved by human experts according to simple heuristics, which are rarely optimal. This paper introduces an automatic, data-driven and heuristic-free approach to optimize robot search strategies. By training a neural model of the search strategy on a large set of simulated stochastic environments, conditioning it on few real-world examples and inverting the model, we can infer search strategies which adapt to the time-variant characteristics of the underlying probability distributions, while requiring very few real-world measurements. We evaluate our approach on two different industrial robots in the context of spiral and probe search for THT electronics assembly.Comment: 7 pages, 5 figures, accepted to the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japan For code and data, see https://github.com/benjaminalt/dps

The coating of a NiTi alloy has a greater impact on the mechanical properties than the acidity of Saliva

The aim of this study was to evaluate the influence of the acidity of saliva on changes to the surface roughness, friction and microhardness of NiTi alloys with various coatings. Three types of commercially available NiTi archwires: uncoated, rhodium coated and nitrified (dimension 0.508 70.508 mm, 10 cm long) were immersed in 10 mL of artificial saliva with the pH ranging from 4.8 to 6.6 for a period of 28 d. Surface roughness, friction and microhardness were analyzed and compared to the unexposed as-received wires. These mechanical properties were influenced by the wire coating with a moderate-to-high effect size (p\ua30.005; h=0.132-0.309). The uncoated wire had a lower maximum roughness depth after exposure to pH 6.6 and 5.5 than the unexposed wire (p=0.026; h=0.346). The friction was significantly increased only in the rhodium-coated NiTi at pH 4.8 compared to the lower acidities and the unexposed wire (p=0.005; h=0.437). No correlation was found between pH, surface roughness, friction and microhardness, respectively. The coating of a NiTi alloy has a greater impact on the mechanical proper- ties than the acidity does. A rhodium coating makes the alloy harder, induces a rougher surface and more friction. Nitrification does not alter the alloy as much. The relation between acidity and mechanical properties is not linear. A high acidity of 4.8 induces a high friction, but only in rhodium-coated NiTi. A lower acidity does not change the friction significantly

EfficientPPS: Part-aware Panoptic Segmentation of Transparent Objects for Robotic Manipulation

The use of autonomous robots for assistance tasks in hospitals has the potential to free up qualified staff and im-prove patient care. However, the ubiquity of deformable and transparent objects in hospital settings poses signif-icant challenges to vision-based perception systems. We present EfficientPPS, a neural architecture for part-aware panoptic segmentation that provides robots with semantically rich visual information for grasping and ma-nipulation tasks. We also present an unsupervised data collection and labelling method to reduce the need for human involvement in the training process. EfficientPPS is evaluated on a dataset containing real-world hospital objects and demonstrated to be robust and efficient in grasping transparent transfusion bags with a collaborative robot arm

LapSeg3D: Weakly Supervised Semantic Segmentation of Point Clouds Representing Laparoscopic Scenes

The semantic segmentation of surgical scenes is a prerequisite for task automation in robot assisted interventions. We propose LapSeg3D, a novel DNN-based approach for the voxel-wise annotation of point clouds representing surgical scenes. As the manual annotation of training data is highly time consuming, we introduce a semi-autonomous clustering-based pipeline for the annotation of the gallbladder, which is used to generate segmented labels for the DNN. When evaluated against manually annotated data, LapSeg3D achieves an F1 score of 0.94 for gallbladder segmentation on various datasets of ex-vivo porcine livers. We show LapSeg3D to generalize accurately across different gallbladders and datasets recorded with different RGB-D camera systems

LapSeg3D: Weakly Supervised Semantic Segmentation of Point Clouds Representing Laparoscopic Scenes

The semantic segmentation of surgical scenes is a prerequisite for task automation in robot assisted interventions. We propose LapSeg3D, a novel DNN-based approach for the voxel-wise annotation of point clouds representing surgical scenes. As the manual annotation of training data is highly time consuming, we introduce a semi-autonomous clustering-based pipeline for the annotation of the gallbladder, which is used to generate segmented labels for the DNN. When evaluated against manually annotated data, LapSeg3D achieves an F1 score of 0.94 for gallbladder segmentation on various datasets of ex-vivo porcine livers. We show LapSeg3D to generalize accurately across different gallbladders and datasets recorded with different RGB-D camera systems.Comment: 6 pages, 5 figures, accepted at the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japa

RoboGrind: Intuitive and Interactive Surface Treatment with Industrial Robots

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. It combines a sophisticated 3D perception pipeline for surface scanning and automatic defect identification, an interactive voice-controlled wizard system for the AI-assisted bootstrapping and parameterization of robot programs, and an automatic planning and execution pipeline for force-controlled robotic surface treatment. RoboGrind is evaluated both under laboratory and real-world conditions in the context of refabricating fiberglass wind turbine blades.Comment: 7 pages, 6 figures, accepted to the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024