This paper presents a rotation-invariant embedded platform for simulating (neural) cellular automata (NCA) in modular robotic systems. Inspired by previous work on physical NCA, we introduce key innovations that overcome limitations in prior hardware designs. Our platform features a symmetric, modular structure, enabling seamless connections between cells regardless of orientation. Additionally, each cell is battery-powered, allowing it to operate independently and retain its state even when disconnected from the collective. To demonstrate the platform's applicability, we present a novel rotation-invariant NCA model for isotropic shape classification. The proposed system provides a robust foundation for exploring the physical realization of NCA, with potential applications in distributed robotic systems and self-organizing structures.

Conventional time-series generation often ignores domain-specific physical constraints, limiting statistical and physical consistency. We propose a hierarchical framework that embeds the inherent hierarchy of physical laws-conservation, dynamics, boundary, and empirical relations-directly into deep generative models, introducing a new paradigm of physics-informed inductive bias. Our method combines Fourier Neural Operators (FNOs) for learning physical operators with Conditional Flow Matching (CFM) for probabilistic generation, integrated via time-dependent hierarchical constraints and FNO-guided corrections. Experiments on harmonic oscillators, human activity recognition, and lithium-ion battery degradation show 16.3% higher generation quality, 46% fewer physics violations, and 18.5% improved predictive accuracy over baselines.

We develop a learning-augmented online algorithm that makes decisions based on (potentially inaccurate) predicted lengths of the idle periods. The algorithm's performance is near-optimal when predictions are accurate and

Datasets of complex cyber-physical systems (CPS) are difficult to build and large CPS datasets are not publicly available.

Battery health prognostics are critical for ensuring safety, efficiency, and sustainability in modern energy systems. However, it has been challenging to achieve accurate and robust prognostics due to complex battery degradation behaviors with nonlinearity, noise, capacity regeneration, etc. Existing data-driven models capture temporal degradation features but often lack knowledge guidance, which leads to unreliable long-term health prognostics. To overcome these limitations, we propose Karma, a knowledge-aware model with frequency-adaptive learning for battery capacity estimation and remaining useful life prediction. The model first performs signal decomposition to derive battery signals in different frequency bands. A dual-stream deep learning architecture is developed, where one stream captures long-term low-frequency degradation trends and the other models high-frequency short-term dynamics. Karma regulates the prognostics with knowledge, where battery degradation is modeled as a double exponential function based on empirical studies. Our dual-stream model is used to optimize the parameters of the knowledge with particle filters to ensure physically consistent and reliable prognostics and uncertainty quantification. Experimental study demonstrates Karma's superior performance, achieving average error reductions of 50.6% and 32.6% over state-of-the-art algorithms for battery health prediction on two mainstream datasets, respectively. These results highlight Karma's robustness, generalizability, and potential for safer and more reliable battery management across diverse applications.

Abstract-- This paper investigates the integration of haptic feedback and virtual reality (VR) control interfaces to enhance teleoperation and telemanipulation of underwater ROVs (remotely operated vehicles). Traditional ROV teleoperation relies on low-resolution 2D camera feeds and lacks immersive and sensory feedback, which diminishes situational awareness in complex subsea environments. We propose SubSense - a novel VR-Haptic framework incorporating a non-invasive feedback interface to an otherwise 1-DOF (degree of freedom) manipulator, which is paired with the teleoperator's glove to provide haptic feedback and grasp status. Our results highlight the potential of multisensory feedback in immersive virtual environments to significantly improve remote situational awareness and mission performance, offering more intuitive and accessible ROV operations in the field. Remotely Operated V ehicles (ROVs) are indispensable tools in the marine industry, offering a safer and more cost-effective alternative to human divers [1]. Underwater ROVs are versatile platforms supporting a range of missions, from routine imaging and infrastructure inspection to complex tasks such as environmental monitoring [2], maintaining sub-sea infrastructure [3], [4], performing mine countermeasure and explosive ordinance disposal [5], salvaging, search-and-rescue [6], and deep-water expeditions [7]. With over 79% of subsea deployments done by ROVs, they play a crucial role in commerce, military, and science - enabling us to explore beyond the limits of human scuba divers [8]. Despite growing industrial demands and recent advancements, underwater ROVs still have inherent limitations, particularly in their immersive control and interaction capabilities.

A recent Schmidt Ocean Institute expedition off the coast of Uruguay discovered at least 30 suspected new species and explored a sunken warship. An octopus moves around deep-sea corals at 1,612 meters (about 5,288 feet) during a remotely operated vehicle, or ROV, dive near the historic HMS'Challenger's' oceanographic station 320, where the country's first coral samples were collected almost 150 years ago. Breakthroughs, discoveries, and DIY tips sent every weekday. An expedition led by a team of scientists from Uruguay discovered that the South American nation's deep-sea coral reefs are thriving and teeming with life. The reefs are primarily home to numerous species that were recently listed as vulnerable to extinction.

Abstract--End-of-Life (EoL) phones significantly exacerbate global e-waste challenges due to their high production volumes and short lifecycles. Disassembly is among the most critical processes in EoL phone recycling. However, it relies heavily on human labor due to product variability. Consequently, the manual process is both labor-intensive and time-consuming. In this paper, we propose a low-cost, easily deployable automated and selective disassembly and sorting system for EoL phones, consisting of three subsystems: an adaptive cutting system, a vision-based robotic sorting system, and a battery removal system. The system can process over 120 phones per hour with an average disassembly success rate of 98.9%, efficiently delivering selected high-value components to downstream processing. It provides a reliable and scalable automated solution to the pressing challenge of EoL phone disassembly. Additionally, the automated system can enhance disassembly economics, converting a previously unprofitable process into one that yields a net profit per unit weight of EoL phones. E-waste presents a global challenge due to its rapid growth, high resource value, and the severe environmental and health risks from improper recycling and hazardous substances [1-3]. Global e-waste surged to a record 62 million tonnes in 2022 and is expected to reach 82 million tonnes by 2030 [4]. Recycling converts e-waste components into valuable raw materials, which is critical for addressing the escalating e-waste problem and supporting a sustainable circular economy [5-10]. Nevertheless, only 22.3 % of e-waste was recorded as recycled in 2022 [4]. The high human labor cost and health risk concerns are the major challenges associated with the recycling process [11]. This material is based upon work supported by the REMADE Institute, USA (21-01-RM-5083).