HybridoNet-Adapt: A Domain-Adapted Framework for Accurate Lithium-Ion Battery RUL Prediction

Accurate prediction of the remaining useful life (RUL) in Lithium-ion battery (LIB) health management systems is crucial for ensuring reliability and safety. Current methods typically assume that training and testing data share the same distribution, overlooking the benefits of incorporating diverse data sources to enhance model performance. To address this limitation, we introduce a data-independent RUL prediction framework along with its domain adaptation (DA) approach, which leverages heterogeneous data sources for improved target predictions. Our approach integrates comprehensive data preprocessing, including feature extraction, denoising, and normalization, with a data-independent prediction model that combines Long Short-Term Memory (LSTM), Multihead Attention, and a Neural Ordinary Differential Equation (NODE) block, termed HybridoNet. The domain-adapted version, HybridoNet Adapt, is trained using a novel technique inspired by the Domain-Adversarial Neural Network (DANN) framework, a regression ensemble method, and Maximum Mean Discrepancy (MMD) to learn domain-invariant features from labeled cycling data in the source and target domains. Experimental results demonstrate that our approach outperforms state-of-the-art techniques, providing reliable RUL predictions for real-world applications.

RoboDesign1M: A Large-scale Dataset for Robot Design Understanding

Robot design is a complex and time-consuming process that requires specialized expertise. Gaining a deeper understanding of robot design data can enable various applications, including automated design generation, retrieving example designs from text, and developing AI-powered design assistants. While recent advancements in foundation models present promising approaches to addressing these challenges, progress in this field is hindered by the lack of large-scale design datasets. In this paper, we introduce RoboDesign1M, a large-scale dataset comprising 1 million samples. Our dataset features multimodal data collected from scientific literature, covering various robotics domains. We propose a semi-automated data collection pipeline, enabling efficient and diverse data acquisition. To assess the effectiveness of RoboDesign1M, we conduct extensive experiments across multiple tasks, including design image generation, visual question answering about designs, and design image retrieval. The results demonstrate that our dataset serves as a challenging new benchmark for design understanding tasks and has the potential to advance research in this field. RoboDesign1M will be released to support further developments in AI-driven robotic design automation.