Adept manipulation of articulated objects is essential for robots to operate successfully in human environments. Such manipulation requires both effectiveness—reliable operation despite uncertain object structures—and efficiency—swift execution with minimal redundant steps and smooth trajectories. Existing approaches struggle to achieve both objectives simultaneously: methods relying on predefined kinematic models lack robustness when encountering structural variations, while the tactile-informed approach achieves robust manipulation but sacrifices efficiency through reactive, step-by-step execute-and-recover cycles. To address this challenge, this paper introduces TacMan-Turbo, a proactive tactile control framework that unifies the cycles into a continuous control loop. Our key insight is to interpret tactile signals temporally in addition to spatially: instead of treating contact deviations merely as instantaneous error signals requiring immediate compensation, we analyze sequential tactile observations to reveal local kinematic information. This temporal perspective enables our controller to predict future object states and proactively modulate manipulation velocities, eliminating the previous execute-and-recover cycle. In evaluations across 200 diverse simulated articulated objects and real-world experiments, our approach maintains a near-perfect performance while significantly enhancing time efficiency, action efficiency, and trajectory smoothness (all p-values < 0.0001). These results demonstrate that tactile feedback serves a dual purpose: providing not only spatial error signals for reactive control but also critical temporal structural information that enables proactive prediction and control. By enabling robots to manipulate articulated objects both reliably and efficiently, this work advances robot capabilities for seamless operation in dynamic, human-centric environments.

Vehicle aerodynamics optimization has become critical for automotive electrification, where drag reduction directly determines electric vehicle range and energy efficiency. Traditional approaches face an intractable trade-off: computationally expensive Computational Fluid Dynamics (CFD) simulations requiring weeks per design iteration, or simplified models that sacrifice production-grade accuracy. While machine learning offers transformative potential, existing datasets exhibit fundamental limitations -- inadequate mesh resolution, missing vehicle components, and validation errors exceeding 5% -- preventing deployment in industrial workflows. We present DrivAerStar, comprising 12,000 industrial-grade automotive CFD simulations generated using STAR-CCM+${}^{\textregistered}$ software. The dataset systematically explores three vehicle configurations through 20 Computer Aided Design (CAD) parameters via Free Form Deformation (FFD) algorithms, including complete engine compartments and cooling systems with realistic internal airflow. DrivAerStar achieves wind tunnel validation accuracy below 1.04% -- a five-fold improvement over existing datasets -- through refined mesh strategies with strict wall $y^+$ control. Benchmarks demonstrate that models trained on this data achieve production-ready accuracy while reducing computational costs from weeks to minutes. This represents the first dataset bridging academic machine learning research and industrial CFD practice, establishing a new standard for data-driven aerodynamic optimization in automotive development. Beyond automotive applications, DrivAerStar demonstrates a paradigm for integrating high-fidelity physics simulations with Artificial Intelligence (AI) across engineering disciplines where computational constraints currently limit innovation.