Handling novel printed circuit boards (PCBs) in industrial robotic systems presents persistent challenges in adaptability, precision, and real-time reconfiguration. Traditional systems often require extensive reprogramming and manual intervention when new PCB variants are introduced—resulting in costly downtime and limited flexibility. To address this, we present a novel Behavior Tree (BT)-driven reconfiguration framework that integrates multimodal human-robot interfaces—including vision-based proposals, UI-guided corrections, and kinesthetic demonstrations—into the task execution logic. Unlike existing approaches that treat human input as an external override, our framework embeds these modalities directly into BT control flows and links them to a persistent Knowledge Base for runtime adaptation and learning. Experimental validation on a real robotic workcell demonstrated that full reconfiguration can be completed in under 2.5 minutes, significantly reducing setup time and programming effort. By combining modular BT execution with intuitive human interaction, the proposed system enables scalable, user-adaptive automation for dynamic, high-mix industrial environments.

To automate 3D surface processing across diverse category-level objects it is imperative to represent process-related region of interest (P-ROI), which is not obtained with conventional keypoint or semantic part correspondences. To resolve this issue, we propose Neural Region Descriptor Fields (NRDF) for achieving unsupervised dense 3D surface region correspondence such that arbitrary ROI is retrieved for a new instance of a known category of object. We utilize the NRDF representation as a medium to facilitate one-shot P-ROI level process knowledge transfer. Recent developments in implicit 3D object representations have focused on keypoint or part correspondences, which have resulted in applications like robotic grasping and manipulation. However, explicit one-shot P-ROI correspondence, and its application for 3D surface process knowledge transfer, is treated for the first time in this work, to the best of our knowledge. The evaluation results show that the proposed approach outperforms the dense correspondence baselines in implicit shape representation and the capacity to retrieve matching arbitrary ROIs. In addition, we validate the practicality of our proposed system in a real-world robotic surface processing application. Our code is available at https://github.com/Profactor/Neural-Region-Descriptor-Fields.

The recent developments of AI and deep learning, together with increased data availability, have made noticeable progress in the field of robotic object grasping. However, the performance of state-of-the-art models in predicting reliable grasp for a given set of novel industrial objects, especially from a different domain with respect to the training data, still needs improvement. In this regard, we propose a novel approach of applying domain adaptation to the novel object synthesis by generating evolved objects. This proposed approach addresses the use case scenario of grasping a newly introduced set of industrial objects and an associated lack of training data. To realize the aforementioned domain adaptation, we apply genetic algorithm based on the method in Evolved Grasping Analysis Dataset (EGAD). Here we modify it to generate evolved objects from a given complex object rather than generating random objects, and the modified algorithm is referred to as EGAD-COMPLEX (EGAD-CMPLX). Our evaluation results show that for a given test set of novel target domain industrial objects, the grasp prediction model developed using the proposed evolved objects and the EGAD objects have superior performance. Specifically, the proposed model have, on average, 16 percent better grasp success rate than the baseline model.

In a goal-oriented Human Robot Collaborative (HRC) scenario, where the goal is to complete an assembly process, a robust object tracker might not necessarily fulfill its functional role due to the dynamic nature of HRC. Moreover, for an efficient HRC, the functional role of the object tacker should not only be limited to localizing and tracking objects for robotic manipulation. It should also help to determine the current state of the assembly process and verify if the chosen action has been successfully performed and thus to enable an uninterrupted completion of an HRC assembly process. We present a Context Enhanced Framework for Multi Object Tracking, that i) allows uninterrupted completion of an assembly process, ii) improves the overall functional accuracy of the object tracker from 49 percent to 96 percent, and iii) enables the object tracker to handle multiple instance of multiple objects in a HRC setting.

Commodity RGB-D sensors capture color images along with dense pixel-wise depth information in real-time. Typical RGB-D sensors are provided with a factory calibration and exhibit erratic depth readings due to coarse calibration values, ageing and thermal influence effects. This limits their applicability in computer vision and robotics. We propose a novel method to accurately calibrate depth considering spatial and thermal influences jointly. Our work is based on Gaussian Process Regression in a four dimensional Cartesian and thermal domain. We propose to leverage modern GPUs for dense depth map correction in real-time. For reproducibility we make our dataset and source code publicly available.

In this paper, a robust, real-time object tracking approach capable of dealing with multiple symmetric and non-symmetric objects in a real-time requirement setting is proposed. The approach relies only on depth data to track multiple objects in a dynamic environment and uses random-forest based learning to deal with problems like object occlusion, motion-blur due to camera motion and clutter. We show that the relation between object motion and the corresponding change in its 3D point cloud data can be learned using only 6 random forests. A framework that unites object pose estimation and object pose tracking to efficiently track multiple objects in 3D space is presented. The approach is robust in tracking objects even in presence of motion blur that causes noisy depth data and is capable of real-time performance with 1.8ms per frame. The experimental evaluations demonstrate the performance of the approach against robustness, accuracy and speed and compare the approach with the state of the art. A publicly available dataset with real-world data is also provided for future benchmarking.

The robotic system integrator's dream of (re)using existing software components and benefiting from a common software framework for service orchestration becomes more and more evident. Current initiatives tend to promote the own framework or class library and do not put a lot of effort into illustrating how to integrate existing functionality from other frameworks. Within joint projects between industry and scientific partners, we typically face the challenge of having several development teams using different techniques, class libraries and open source frameworks. In this context, we want to present our development approach using 4DIAC development tools and explicitly highlight useful extensions added for a smooth integration of heterogeneous components of various frameworks which is shown by examples.

Adaptive manufacturing is one of the most important objectives of the MANUFUTURE technology platform. To realize the vision of zero downtime, automation and control systems that react robust on system faults and changes by reconfiguration need improvements, mainly on the control and software level. A reconfigurable manufacturing system has the ability to easily and repeatedly change its structure by adding or removing hardware and/or software components in order to optimally adapt to fast changes in product requirements. This paper presents an adaptive and reconfigurable control framework which enables production systems to react more flexible on changes in the product demands. Concepts and requirements that will be needed for such a framework will be highlighted. Furthermore, a first approach for a control framework will be presented.

This paper describes a computational model for deriving a decomposition of objects from laser rangefinder data. The process aims to produce a set of parts defined by compactness and smoothness of surface connectivity. Relying on a general decomposition rule, any kind of objects made up of free-form surfaces are partitioned. A robust method to partition the object based on Markov random fields (MRF), which allows to incorporate prior knowledge, is presented. Shape index and curvedness descriptors along with discontinuity and concavity distributions are introduced to classify region labels correctly. In addition, a novel way to classify the shape of a surface is proposed resulting in a better distinction of concave, convex and saddle shapes. To achieve a reliable classification a multiscale method provides a stable estimation of the shape index.

This paper presents an approach to detect classes of features that are relevant for automating spray painting. Using knowledge about the painting process a set of elementary geometries is defined, where each elementary geometry is related to a specific painting strategy. Hence all parts and part families containing these elementary geometries can be detected. After detection the paint strokes are automatically generated for robot programming. Specifically we show how free-form surfaces, cavities and rib sections are detected in the range image of the parts. Results of detecting these features on a large variety of parts are presented.

Today's industrial automation of spray painting is limited to high part volumes and robot trajectories that are programmed by off-line programming and manual teach-in. This paper presents an approach that uses range image data to obtain the geometry of an unknown part and to automatically generate the robot spray painting trajectories. Laser strip range sensors are installed in front of the paint booth to acquire a range image of the part. Utilizing process knowledge (a geometric library containing constraints specific for the painting application) geometric primitives are detected in the range data. From the geometric primitives a normal vector field is generated that enables to extract main faces. The main faces are located in a 3D space and the process knowledge related to each geometric primitive is utilized to obtain the trajectory for the paint gun. Results of painting a car mirror and steering column are given.

We present a method employing hybrid force and vision based control to effect a sequence of contact manipulations. Instead of requiring a-priori object and environment models, force sensing is used to simultaneously update a surface model while controlling the manipulator. This is incorporated into an uncalibrated visual servoing system, which also estimates the visual-motor coordinate transform. The result is a hybrid force-vision controller which does not need any a-priori robot, camera, object or environment models. The approach is validated experimentally using an IMI Zebra robot arm.