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Abstract--Autonomous systems increasingly rely on human feedback to align their behavior, expressed as pairwise comparisons, rankings, or demonstrations. While existing methods can adapt behaviors, they often fail to guarantee safety in safety-critical domains. We propose a safety-guaranteed, optimal, and efficient approach to solve the learning problem from preferences, rankings, or demonstrations using Weighted Signal T emporal Logic (WSTL). WSTL learning problems, when implemented naively, lead to multi-linear constraints in the weights to be learned. By introducing structural pruning and log-transform procedures, we reduce the problem size and recast the problem as a Mixed-Integer Linear Program while preserving safety guarantees. Experiments on robotic navigation and real-world Formula 1 data demonstrate that the method effectively captures nuanced preferences and models complex task objectives. Autonomous systems are increasingly part of our daily lives, from driverless cars in urban navigation to household robots performing domestic chores. Since these systems operate closely alongside humans, learning from human feedback is a natural way to ensure their behaviors align with human desires.

This content is not available in your location. Nadine de Klerk hits 84 off 54 balls as South Africa recover from 81-5 to chase down their target of 252 with seven balls to spare, securing a famous three wicket win against hosts India at the ICC Women's Cricket World Cup. 'I was asking ChatGPT is this real?' - Fraser & Tulloch on making black history. Video, 00:04:27 'I was asking ChatGPT is this real?' - Fraser & Tulloch on making black history'We've got mountains to do' - Cavallo on homophobia in football. Video, 00:01:58 'We've got mountains to do' - Cavallo on homophobia in football We have already lost too many games - Mahomes.

Boundaries, drops and missed run-out chances - Ghosh's remarkable innings This content is not available in your location. Richa Ghosh's 94 runs off 77 balls, including 15 boundaries, helps save India's innings as they recover from 102-6 to reach 251-8 against South Africa in their ICC Women's Cricket World Cup match. Boundaries, drops and missed run-out chances - Ghosh's remarkable innings. Video, 00:03:29 Boundaries, drops and missed run-out chances - Ghosh's remarkable innings'I was asking ChatGPT is this real?' - Fraser & Tulloch on making black history. Video, 00:04:27 'I was asking ChatGPT is this real?' - Fraser & Tulloch on making black history'We've got mountains to do' - Cavallo on homophobia in football.

In response to recent FIA regulations reducing Formula 1 team wind tunnel hours (from 320 hours for last-place teams to 200 hours for championship leaders) and strict budget caps of 135 million USD per year, more efficient aerodynamic development tools are needed by teams. Conventional computational fluid dynamics (CFD) simulations, though offering high fidelity results, require large computational resources with typical simulation durations of 8-24 hours per configuration analysis. This article proposes a Physics-Informed Neural Network (PINN) for the fast prediction of Formula 1 front wing aerodynamic coefficients. The suggested methodology combines CFD simulation data from SimScale with first principles of fluid dynamics through a hybrid loss function that constrains both data fidelity and physical adherence based on Navier-Stokes equations. Training on force and moment data from 12 aerodynamic features, the PINN model records coefficient of determination (R-squared) values of 0.968 for drag coefficient and 0.981 for lift coefficient prediction while lowering computational time. The physics-informed framework guarantees that predictions remain adherent to fundamental aerodynamic principles, offering F1 teams an efficient tool for the fast exploration of design space within regulatory constraints.

W e introduce VROOM, a system for reconstructing 3D models of F ormula 1 circuits using only onboard camera footage from racecars. Leveraging video data from the 2023 Monaco Grand Prix, we address video challenges such as high-speed motion and sharp cuts in camera frames. Our pipeline analyzes different methods such as DROID-SLAM, AnyCam, and Monst3r and combines preprocessing techniques such as different methods of masking, temporal chunking, and resolution scaling to account for dynamic motion and computational constraints. W e show that Vroom is able to partially recover track and vehicle trajectories in complex environments. These findings indicate the feasibility of using onboard video for scalable 4D reconstruction in real-world settings.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. Formula 1 and Amazon Web Services (AWS) have been partners for more than six years. But, that longstanding partnership is now set to reach new heights as the popular sports league and the leading tech company will leverage AWS tools to develop a generative artificial intelligence-designed trophy for the upcoming Canadian Grand Prix. The first-of-its-kind approach to the trophy for the highly-anticipated event is expected to help increase creativity.