Buildings account for a significant share of global energy consumption and emissions, making it critical to operate them efficiently. As electricity grids become more volatile with renewable penetration, buildings must provide flexibility to support grid stability. Building automation plays a key role in enhancing efficiency and flexibility via centralized operations, but it must prioritize occupant-centric strategies to balance energy and comfort targets. However, incorporating occupant information into large-scale, centralized building operations remains challenging due to data limitations. We investigate the potential of using whole-building smart meter data to infer both occupancy and system operations. Integrating these insights into data-driven building energy analysis allows more occupant-centric energy-saving and flexibility at scale. Specifically, we propose OccuEMBED, a unified framework for occupancy inference and system-level load analysis. It combines two key components: a probabilistic occupancy profile generator, and a controllable and interpretable load disaggregator supported by Kolmogorov-Arnold Networks (KAN). This design embeds knowledge of occupancy patterns and load-occupancy-weather relationships into deep learning models. We conducted comprehensive evaluations to demonstrate its effectiveness across synthetic and real-world datasets compared to various occupancy inference baselines. OccuEMBED always achieved average F1 scores above 0.8 in discrete occupancy inference and RMSE within 0.1-0.2 for continuous occupancy ratios. We further demonstrate how OccuEMBED integrates with building load monitoring platforms to display occupancy profiles, analyze system-level operations, and inform occupant-responsive strategies. Our model lays a robust foundation in scaling occupant-centric building management systems to meet the challenges of an evolving energy system.

The advanced bidirectional EV charging and discharging technology, aimed at supporting grid stability and emergency operations, has driven a growing interest in workplace applications. It not only effectively reduces electricity expenses but also enhances the resilience of handling practical issues, such as peak power limitation, fluctuating energy prices, and unpredictable EV departures. However, existing EV charging strategies have yet to fully consider these factors in a way that benefits both office buildings and EV users simultaneously. To address these issues, we propose HUCA, a novel real-time charging control for regulating energy demands for both the building and electric vehicles. HUCA employs hierarchical actor-critic networks to dynamically reduce electricity costs in buildings, accounting for the needs of EV charging in the dynamic pricing scenario. To tackle the uncertain EV departures, a new critic augmentation is introduced to account for departure uncertainties in evaluating the charging decisions, while maintaining the robustness of the charging control. Experiments on real-world electricity datasets under both simulated certain and uncertain departure scenarios demonstrate that HUCA outperforms baselines in terms of total electricity costs while maintaining competitive performance in fulfilling EV charging requirements. A case study also manifests that HUCA effectively balances energy supply between the building and EVs based on real-time information.

Heating and lighting buildings requires a vast amount of energy: 18% of all global energy consumption, according to the International Energy Agency. Contributing to the problem is the fact that many buildings' HVAC systems are outdated and slow to respond to weather changes, which can lead to severe energy waste. Some scientists and technologists are hoping that AI can solve that problem. At the moment, much attention has been drawn to the energy-intensive nature of AI itself: Microsoft, for instance, acknowledged that its AI development has imperiled their climate goals. But some experts argue that AI can also be part of the solution by helping make large buildings more energy-efficient.

Kurt Knutsson shows how this companion bot can act like a home security guard and life alert if you have fallen and can't get help on your own. If you are looking for some weird and, in some cases, bizarre tech that will blow your mind, you have come to the right place. We've compiled some of the most fascinating and futuristic gadgets that have wowed us over the past year. From a hamster ball robot that can fly and crawl, to a pair of jeans that can protect you from motorcycle accidents to an AI-powered wearable gadget, these are some of the 10 coolest and craziest things you will ever see. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER The latest sensation in robotics is the Hybrid Mobility Robot (HMR) from Revolute Robotics.

We describe a system for deep reinforcement learning of robotic manipulation skills applied to a large-scale real-world task: sorting recyclables and trash in office buildings. Real-world deployment of deep RL policies requires not only effective training algorithms, but the ability to bootstrap real-world training and enable broad generalization. To this end, our system combines scalable deep RL from real-world data with bootstrapping from training in simulation, and incorporates auxiliary inputs from existing computer vision systems as a way to boost generalization to novel objects, while retaining the benefits of end-to-end training. We analyze the tradeoffs of different design decisions in our system, and present a large-scale empirical validation that includes training on real-world data gathered over the course of 24 months of experimentation, across a fleet of 23 robots in three office buildings, with a total training set of 9527 hours of robotic experience. Our final validation also consists of 4800 evaluation trials across 240 waste station configurations, in order to evaluate in detail the impact of the design decisions in our system, the scaling effects of including more real-world data, and the performance of the method on novel objects. The projects website and videos can be found at \href{http://rl-at-scale.github.io}{rl-at-scale.github.io}.

Reinforcement learning (RL) can enable robots to learn complex behaviors through trial-and-error interaction, getting better and better over time. Several of our prior works explored how RL can enable intricate robotic skills, such as robotic grasping, multi-task learning, and even playing table tennis. Although robotic RL has come a long way, we still don't see RL-enabled robots in everyday settings. The real world is complex, diverse, and changes over time, presenting a major challenge for robotic systems. However, we believe that RL should offer us an excellent tool for tackling precisely these challenges: by continually practicing, getting better, and learning on the job, robots should be able to adapt to the world as it changes around them.

Problem Setup We study the problem of continual real-world reinforcement learning through the lenses of a large scale experiment, where we deployed a fleet of 23 RL-enabled robots over two years in Google office buildings to sort waste and recycling. In our experiment, a robot roamed around an office building searching for "waste stations" (bins for recyclables, compost, and trash). The robot was tasked with approaching each waste station to sort it, moving items between the bins so that all recyclables (cans, bottles, etc.) were placed in the recyclable bin, all the compostable items (cardboard containers, paper cups, etc.) were placed in the compost bin, and everything else was placed in the landfill trash bin. The task of sorting waste is much harder than it sounds: not only does the robot need to correctly pick up the vast variety of objects that people deposit into waste bins, but it also needs to identify the appropriate bin for each object and sort them as quickly and efficiently as possible. The experiment setup enabled robots to learn on the job and improve through real-world experience, additional autonomous data collection in "robot classrooms," and simulation.

Kurt "CyberGuy" Knutsson explains whether robot security guards are better or worse for society. AI technology seems to be finding its way into every industry from fast-food chains to delivering packages to automatic self-driving vehicles. Now, some companies are also incorporating AI security guards to keep their businesses safe. However, I'm not so sure these bots can be reliable. Let's see how the robot security experiments are turning into reality.

Vending machines, which allow people to easily purchase items without interacting with a human worker, have been around since the 1st century, when a Greek engineer and mathematician named Hero Alexandria created a machine that accepted a coin before dispensing holy water at a temple, to prevent people from taking more than their share of holy water. Two millennia later, a far greater number and variety of products can be purchased from vending machines, thanks in part to the advent of new technologies including always-on, Internet of Things (IoT) connectivity, advanced physical and digital controls that allow these machines to be placed in a wide variety of settings, and the use of artificial intelligence (AI)-based algorithms that can capture and analyze customer insights, improve stocking efficiency, and deliver greater levels of personalization to customers. The global installed base of connected vending machines reached an estimated 2.4 million units in 2019, according to Berg Insight, a research firm that tracks the installed base of connected vending machines. Connected vending machines are equipped with an always-on Internet connection, which allows data to be sent between machines in the field and management software, enabling real-time payments, monitoring, and remote management of the machines. Advanced feature sets and functionality are projected to drive the market to nearly nine million units by 2024, according to Berg Insight, helped along by the desire of organizations to better serve customers without needing to attract and retain relatively costly human workers.

Penn State researchers are looking for a safer and more efficient way to use machine learning in the real world. Using a simulated high-rise office building, they developed and tested a new reinforcement learning algorithm aimed at improving energy consumption and occupant comfort in a real-world setting. Greg Pavlak, assistant professor of architectural engineering at Penn State, presented the results from the paper he co-authored, "Constrained Differentiable Cross-Entropy Method for Safe Model-Based Reinforcement Learning," at the Association for Computing Machinery International Conference on Systems for Energy-Efficient Built Environments (BuildSys) Conference, which was held Nov. 9-10 in Boston. "Reinforcement learning agents explore their environments to learn optimal actions through trial and error," Pavlak said. "Due to challenges in simulating the complexities of the real world, there is a growing trend to train reinforcement learning agents directly in the real world instead of in simulation."