Automated Active Learning with a Robot

In the field of automated processes in industry, a major goal is for robots to solve new tasks without costly adaptions. Therefore, it is of advantage if the robot can perform new tasks independently while the learning process is intuitively understandable for humans. In this article, we present a highly automated and intuitive active learning algorithm for robots. It learns new classification tasks by asking questions to a human teacher and automatically decides when to stop the learning process by self-assessing its confidence. This so-called stopping criterion is required to guarantee a fully automated procedure. Our approach is highly interactive as we use speech for communication and a graphical visualization tool. The latter provides information about the learning progress and the stopping criterion, which helps the human teacher in understanding the training process better. The applicability of our approach is shown and evaluated on a real Baxter robot

A New Approach to Speeding Up Topic Modeling

Latent Dirichlet allocation (LDA) is a widely-used probabilistic topic modeling paradigm, and recently finds many applications in computer vision and computational biology. In this paper, we propose a fast and accurate batch algorithm, active belief propagation (ABP), for training LDA. Usually batch LDA algorithms require repeated scanning of the entire corpus and searching the complete topic space. To process massive corpora having a large number of topics, the training iteration of batch LDA algorithms is often inefficient and time-consuming. To accelerate the training speed, ABP actively scans the subset of corpus and searches the subset of topic space for topic modeling, therefore saves enormous training time in each iteration. To ensure accuracy, ABP selects only those documents and topics that contribute to the largest residuals within the residual belief propagation (RBP) framework. On four real-world corpora, ABP performs around $10$ to $100$ times faster than state-of-the-art batch LDA algorithms with a comparable topic modeling accuracy.Comment: 14 pages, 12 figure

Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network

With more and more household objects built on planned obsolescence and consumed by a fast-growing population, hazardous waste recycling has become a critical challenge. Given the large variability of household waste, current recycling platforms mostly rely on human operators to analyze the scene, typically composed of many object instances piled up in bulk. Helping them by robotizing the unitary extraction is a key challenge to speed up this tedious process. Whereas supervised deep learning has proven very efficient for such object-level scene understanding, e.g., generic object detection and segmentation in everyday scenes, it however requires large sets of per-pixel labeled images, that are hardly available for numerous application contexts, including industrial robotics. We thus propose a step towards a practical interactive application for generating an object-oriented robotic grasp, requiring as inputs only one depth map of the scene and one user click on the next object to extract. More precisely, we address in this paper the middle issue of object seg-mentation in top views of piles of bulk objects given a pixel location, namely seed, provided interactively by a human operator. We propose a twofold framework for generating edge-driven instance segments. First, we repurpose a state-of-the-art fully convolutional object contour detector for seed-based instance segmentation by introducing the notion of edge-mask duality with a novel patch-free and contour-oriented loss function. Second, we train one model using only synthetic scenes, instead of manually labeled training data. Our experimental results show that considering edge-mask duality for training an encoder-decoder network, as we suggest, outperforms a state-of-the-art patch-based network in the present application context.Comment: This is a pre-print of an article published in Human Friendly Robotics, 10th International Workshop, Springer Proceedings in Advanced Robotics, vol 7. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-89327-3\_16, Springer Proceedings in Advanced Robotics, Siciliano Bruno, Khatib Oussama, In press, Human Friendly Robotics, 10th International Workshop,

Neuromorphic vision based contact-level classification in robotic grasping applications

In recent years, robotic sorting is widely used in the industry, which is driven by necessity and opportunity. In this paper, a novel neuromorphic vision-based tactile sensing approach for robotic sorting application is proposed. This approach has low latency and low power consumption when compared to conventional vision-based tactile sensing techniques. Two Machine Learning (ML) methods, namely, Support Vector Machine (SVM) and Dynamic Time Warping-K Nearest Neighbor (DTW-KNN), are developed to classify material hardness, object size, and grasping force. An Event-Based Object Grasping (EBOG) experimental setup is developed to acquire datasets, where 243 experiments are produced to train the proposed classifiers. Based on predictions of the classifiers, objects can be automatically sorted. If the prediction accuracy is below a certain threshold, the gripper re-adjusts and re-grasps until reaching a proper grasp. The proposed ML method achieves good prediction accuracy, which shows the effectiveness and the applicability of the proposed approach. The experimental results show that the developed SVM model outperforms the DTW-KNN model in term of accuracy and efficiency for real time contact-level classification

Utilizing Reinforcement Learning and Computer Vision in a Pick-And-Place Operation for Sorting Objects in Motion

This master's thesis studies the implementation of advanced machine learning (ML) techniques in industrial automation systems, focusing on applying machine learning to enable and evolve autonomous sorting capabilities in robotic manipulators. In particular, Inverse Kinematics (IK) and Reinforcement Learning (RL) are investigated as methods for controlling a UR10e robotic arm for pick-and-place of moving objects on a conveyor belt within a small-scale sorting facility. A camera-based computer vision system applying YOLOv8 is used for real-time object detection and instance segmentation. Perception data is utilized to ascertain optimal grip points, specifically through an implemented algorithm that outputs optimal grip position, angle, and width. As the implemented system includes testing and evaluation on a physical system, the intricacies of hardware control, specifically the reverse engineering of an OnRobot RG6 gripper is elaborated as part of this study. The system is implemented on the Robotic Operating System (ROS), and its design is in particular driven by high modularity and scalability in mind. The camera-based vision system serves as the primary input, while the robot control is the output. The implemented system design allows for the evaluation of motion control employing both IK and RL. Computation of IK is conducted via MoveIt2, while the RL model is trained and computed in NVIDIA Isaac Sim. The high-level control of the robotic manipulator was accomplished with use of Proximal Policy Optimization (PPO). The main result of the research is a novel reward function for the pick-and-place operation that takes into account distance and orientation from the target object. In addition, the provided system administers task control by independently initializing pick-and-place operation phases for each environment. The findings demonstrate that PPO was able to significantly enhance the velocity, accuracy, and adaptability of industrial automation. Our research shows that accurate control of the robot arm can be reached by training the PPO Model purely by applying a digital twin simulation

FPGA-based High-Performance Collision Detection: An Enabling Technique for Image-Guided Robotic Surgery

Collision detection, which refers to the computational problem of finding the relative placement or con-figuration of two or more objects, is an essential component of many applications in computer graphics and robotics. In image-guided robotic surgery, real-time collision detection is critical for preserving healthy anatomical structures during the surgical procedure. However, the computational complexity of the problem usually results in algorithms that operate at low speed. In this paper, we present a fast and accurate algorithm for collision detection between Oriented-Bounding-Boxes (OBBs) that is suitable for real-time implementation. Our proposed Sweep and Prune algorithm can perform a preliminary filtering to reduce the number of objects that need to be tested by the classical Separating Axis Test algorithm, while the OBB pairs of interest are preserved. These OBB pairs are re-checked by the Separating Axis Test algorithm to obtain accurate overlapping status between them. To accelerate the execution, our Sweep and Prune algorithm is tailor-made for the proposed method. Meanwhile, a high performance scalable hardware architecture is proposed by analyzing the intrinsic parallelism of our algorithm, and is implemented on FPGA platform. Results show that our hardware design on the FPGA platform can achieve around 8X higher running speed than the software design on a CPU platform. As a result, the proposed algorithm can achieve a collision frame rate of 1 KHz, and fulfill the requirement for the medical surgery scenario of Robot Assisted Laparoscopy.published_or_final_versio