118,588 research outputs found
ARMBench: An Object-centric Benchmark Dataset for Robotic Manipulation
This paper introduces Amazon Robotic Manipulation Benchmark (ARMBench), a
large-scale, object-centric benchmark dataset for robotic manipulation in the
context of a warehouse. Automation of operations in modern warehouses requires
a robotic manipulator to deal with a wide variety of objects, unstructured
storage, and dynamically changing inventory. Such settings pose challenges in
perceiving the identity, physical characteristics, and state of objects during
manipulation. Existing datasets for robotic manipulation consider a limited set
of objects or utilize 3D models to generate synthetic scenes with limitation in
capturing the variety of object properties, clutter, and interactions. We
present a large-scale dataset collected in an Amazon warehouse using a robotic
manipulator performing object singulation from containers with heterogeneous
contents. ARMBench contains images, videos, and metadata that corresponds to
235K+ pick-and-place activities on 190K+ unique objects. The data is captured
at different stages of manipulation, i.e., pre-pick, during transfer, and after
placement. Benchmark tasks are proposed by virtue of high-quality annotations
and baseline performance evaluation are presented on three visual perception
challenges, namely 1) object segmentation in clutter, 2) object identification,
and 3) defect detection. ARMBench can be accessed at http://armbench.comComment: To appear at the IEEE Conference on Robotics and Automation (ICRA),
202
MACHINE LEARNING-BASED DRONE AND AERIAL THREAT DETECTION FOR INCREASED TURRET GUNNER SURVIVABILITY
The introduction of aerial drones on the modern battlefield has transformed combat operations, posing a significant threat to ground-based military operations. Detecting drones in safety scenarios is crucial. However, modern machine learning (ML)-based object detectors struggle to detect small objects like drones. This thesis presents three main contributions: (a) data and algorithmic modifications to improve small object detection in YOLO to aid in drone detection, (b) the development of a benchmark drone detection dataset called DyViR, and (c) the implementation of explainable artificial intelligence (XAI) to ensure transparent and trustworthy decision-making. To boost the performance of small object detection, we introduce the Normalized Wasserstein distance (NWD) into the loss function of our ML model. By incorporating this distance metric, we can effectively handle small object detection by reducing the penalty assigned to small objects, and appropriately balancing the significance of different object sizes. This allows the model to prioritize the accurate detection of small objects, enhancing overall performance. To evaluate our algorithm, we developed and tested the DyViR dataset specifically designed for drone detection research. This synthetic dataset provides a benchmark for assessing drone detection performance. In combat settings, the trustworthiness of ML systems is paramount as their decisions impact user survival. Thus, we implemented explainable AI systems (XAI), specifically Grad-CAM and Eigen-CAM, These techniques provide explanations for the models’ decisions, increasing trust in the system for developers and users
Flattening an object algebra to provide performance
Algebraic transformation and optimization techniques have been the method of choice in relational query execution, but applying them in object-oriented (OO) DBMSs is difficult due to the complexity of OO query languages. This paper demonstrates that the problem can be simplified by mapping an OO data model to the binary relational model implemented by Monet, a state-of-the-art database kernel. We present a generic mapping scheme to flatten data models and study the case of straightforward OO model. We show how flattening enabled us to implement a query algebra, using only a very limited set of simple operations. The required primitives and query execution strategies are discussed, and their performance is evaluated on the 1-GByte TPC-D (Transaction-processing Performance Council's Benchmark D), showing that our divide-and-conquer approach yields excellent result
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