I put a bird feeder outside my window a few years ago and it was a fantastic decision. I can look out there and see a wide variety of feathered friends chowing down on the regionally appropriate bird food I provide for them. I also get to see squirrels acting foolish. Right now, Amazon has the Birdfy smart bird feeders on deep discount well before the Prime Day shopping holiday rolls around in early July. This is the easiest possible way to bird watch.

The growth of lithium dendrites significantly impacts the performance and safety of rechargeable batteries, leading to short circuits and capacity degradation. This study proposes a Residual Connection-Enhanced ConvLSTM model to predict dendrite growth patterns with improved accuracy and computational efficiency. By integrating residual connections into ConvLSTM, the model mitigates the vanishing gradient problem, enhances feature retention across layers, and effectively captures both localized dendrite growth dynamics and macroscopic battery behavior. The dataset was generated using a phase-field model, simulating dendrite evolution under varying conditions. Experimental results show that the proposed model achieves up to 7% higher accuracy and significantly reduces mean squared error (MSE) compared to conventional ConvLSTM across different voltage conditions (0.1V, 0.3V, 0.5V). This highlights the effectiveness of residual connections in deep spatiotemporal networks for electrochemical system modeling. The proposed approach offers a robust tool for battery diagnostics, potentially aiding in real-time monitoring and optimization of lithium battery performance. Future research can extend this framework to other battery chemistries and integrate it with real-world experimental data for further validation

Accurate fault detection in lithium-ion batteries is essential for the safe and reliable operation of electric vehicles and energy storage systems. However, existing methods often struggle to capture complex temporal dependencies and cannot fully leverage abundant unlabeled data. Although large language models (LLMs) exhibit strong representation capabilities, their architectures are not directly suited to the numerical time-series data common in industrial settings. To address these challenges, we propose a novel framework that adapts BERT-style pretraining for battery fault detection by extending the standard BERT architecture with a customized time-series-to-token representation module and a point-level Masked Signal Modeling (point-MSM) pretraining task tailored to battery applications. This approach enables self-supervised learning on sequential current, voltage, and other charge-discharge cycle data, yielding distributionally robust, context-aware temporal embeddings. We then concatenate these embeddings with battery metadata and feed them into a downstream classifier for accurate fault classification. Experimental results on a large-scale real-world dataset show that models initialized with our pretrained parameters significantly improve both representation quality and classification accuracy, achieving an AUROC of 0.945 and substantially outperforming existing approaches. These findings validate the effectiveness of BERT-style pretraining for time-series fault detection.

Wybot's solar skimmer does a surprisingly good job of grabbing leaves off the surface of the pool, but its loud operation and poor power management knock it down a peg. Solar-powered pool skimmers flit along the surface of your pool operating under the idea that if they can scoop up debris before it sinks, you won't need to clean the bottom of the pool. It sounds logical, but in practice, most pool skimmers don't do the absolute best of jobs--there's only so much surface area a skimmer can cover before leaves get waterlogged and sink to the depths. But robotic skimmers are better than nothing, especially if you don't have a good in-wall skimmer. The Wybot F1 Pool Skimmer was much more effective at capturing floating leaves than any skimmer I've used to date.

Inorganic synthesis planning currently relies primarily on heuristic approaches or machine-learning models trained on limited datasets, which constrains its generality. We demonstrate that language models, without task-specific fine-tuning, can recall synthesis conditions. Off-the-shelf models, such as GPT-4.1, Gemini 2.0 Flash and Llama 4 Maverick, achieve a Top-1 precursor-prediction accuracy of up to 53.8 % and a Top-5 performance of 66.1 % on a held-out set of 1,000 reactions. They also predict calcination and sintering temperatures with mean absolute errors below 126 °C, matching specialized regression methods. Ensembling these language models further enhances predictive accuracy and reduces inference cost per prediction by up to 70 %. We subsequently employ language models to generate 28,548 synthetic reaction recipes, which we combine with literature-mined examples to pretrain a transformer-based model, SyntMTE. After fine-tuning on the combined dataset, SyntMTE reduces mean-absolute error in sintering temperature prediction to 73 °C and in calcination temperature to 98 °C. This strategy improves models by up to 8.7 % compared with baselines trained exclusively on experimental data. Finally, in a case study on Li7La3Zr2O12 solid-state electrolytes, we demonstrate that SyntMTE reproduces the experimentally observed dopant-dependent sintering trends. Our hybrid workflow enables scalable, data-efficient inorganic synthesis planning.

--This paper proposes an optimization framework that addresses both cycling degradation and calendar aging of batteries for autonomous mobile robot (AMR) to minimize battery degradation while ensuring task completion. A rectangle method of piecewise linear approximation is employed to linearize the bilinear optimization problem. We conduct a case study to validate the efficiency of the proposed framework in achieving an optimal path planning for AMRs while reducing battery aging. Autonomous mobile robots (AMRs) have become increasingly common in industrial and commercial settings, primarily relying on batteries for power in their material handling and transportation tasks. The efficiency and longevity of these battery systems are crucial factors in reducing operational costs and maintenance expenses.

This paper proposes a control-oriented optimization platform for autonomous mobile robots (AMRs), focusing on extending battery life while ensuring task completion. The requirement of fast AMR task planning while maintaining minimum battery state of charge, thus maximizing the battery life, renders a bilinear optimization problem. McCormick envelop technique is proposed to linearize the bilinear term. A novel planning algorithm with relaxed constraints is also developed to handle parameter uncertainties robustly with high efficiency ensured. Simulation results are provided to demonstrate the utility of the proposed methods in reducing battery degradation while satisfying task completion requirements.

Understanding and controlling the atomistic-level reactions governing the formation of the solid-electrolyte interphase (SEI) is crucial for the viability of next-generation solid state batteries. However, challenges persist due to difficulties in experimentally characterizing buried interfaces and limits in simulation speed and accuracy. We conduct large-scale explicit reactive simulations with quantum accuracy for a symmetric battery cell, {\symcell}, enabled by active learning and deep equivariant neural network interatomic potentials. To automatically characterize the coupled reactions and interdiffusion at the interface, we formulate and use unsupervised classification techniques based on clustering in the space of local atomic environments. Our analysis reveals the formation of a previously unreported crystalline disordered phase, Li$_2$S$_{0.72}$P$_{0.14}$Cl$_{0.14}$, in the SEI, that evaded previous predictions based purely on thermodynamics, underscoring the importance of explicit modeling of full reaction and transport kinetics. Our simulations agree with and explain experimental observations of the SEI formations and elucidate the Li creep mechanisms, critical to dendrite initiation, characterized by significant Li motion along the interface. Our approach is to crease a digital twin from first principles, without adjustable parameters fitted to experiment. As such, it offers capabilities to gain insights into atomistic dynamics governing complex heterogeneous processes in solid-state synthesis and electrochemistry.

Correct use of electrical appliances has significantly improved human life quality. Unlike simple tools that can be manipulated with common sense, different parts of electrical appliances have specific functions defined by manufacturers. If we want the robot to heat bread by microwave, we should enable them to review the microwave manual first. From the manual, it can learn about component functions, interaction methods, and representative task steps about appliances. However, previous manual-related works remain limited to question-answering tasks while existing manipulation researchers ignore the manual's important role and fail to comprehend multi-page manuals. In this paper, we propose the first manual-based appliance manipulation benchmark CheckManual. Specifically, we design a large model-assisted human-revised data generation pipeline to create manuals based on CAD appliance models. With these manuals, we establish novel manual-based manipulation challenges, metrics, and simulator environments for model performance evaluation. Furthermore, we propose the first manual-based manipulation planning model ManualPlan to set up a group of baselines for the CheckManual benchmark.

The increasing complexity of modern manufacturing, coupled with demand fluctuation, supply chain uncertainties, and product customization, underscores the need for manufacturing systems that can flexibly update their configurations and swiftly adapt to disturbances. However, current research falls short in providing a holistic reconfigurable manufacturing framework that seamlessly monitors system disturbances, optimizes alternative line configurations based on machine capabilities, and automates simulation evaluation for swift adaptations. This paper presents a dynamic manufacturing line reconfiguration framework to handle disturbances that result in operation time changes. The framework incorporates a system process digital twin for monitoring disturbances and triggering reconfigurations, a capability-based ontology model capturing available agent and resource options, a configuration optimizer generating optimal line configurations, and a simulation generation program initializing simulation setups and evaluating line configurations at approximately 400x real-time speed. A case study of a battery production line has been conducted to evaluate the proposed framework. In two implemented disturbance scenarios, the framework successfully recovers system throughput with limited resources, preventing the 26% and 63% throughput drops that would have occurred without a reconfiguration plan. The reconfiguration optimizer efficiently finds optimal solutions, taking an average of 0.03 seconds to find a reconfiguration plan for a manufacturing line with 51 operations and 40 available agents across 8 agent types.