Search

Combining data-driven models that adapt online and model predictive control (MPC) has enabled effective control of nonlinear systems. However, when deployed on unstable systems, online adaptation may not be fast enough to ensure reliable simultaneous learning and control. For example, controllers on a vehicle executing highly dynamic maneuvers may push the tires to their friction limits, destabilizing the vehicle and allowing modeling errors to quickly compound and cause a loss of control. In this work, we present a Bayesian meta-learning MPC framework. We propose an expressive vehicle dynamics model that leverages Bayesian last-layer meta-learning to enable rapid online adaptation. The model's uncertainty estimates are used to guide informative data collection and quickly improve the model prior to deployment. Experiments on a Toyota Supra show that (i) the framework enables reliable control in dynamic drifting maneuvers, (ii) online adaptation alone may not suffice for zero-shot control of a vehicle at the edge of stability, and (iii) active data collection helps achieve reliable performance.

The skill to drift a car--i.e., operate in a state of controlled oversteer like professional drivers--could give future autonomous cars maximum flexibility when they need to retain control in adverse conditions or avoid collisions. We investigate real-time drifting strategies that put the car where needed while bypassing expensive trajectory optimization. To this end, we design a reinforcement learning agent that builds on the concept of tire energy absorption to autonomously drift through changing and complex waypoint configurations while safely staying within track bounds. We achieve zero-shot deployment on the car by training the agent in a simulation environment built on top of a neural stochastic differential equation vehicle model learned from pre-collected driving data. Experiments on a Toyota GR Supra and Lexus LC 500 show that the agent is capable of drifting smoothly through varying waypoint configurations with tracking error as low as 10 cm while stably pushing the vehicles to sideslip angles of up to 63{\deg}., Comment: Initial submission to ICRA 2025

Model predictive control (MPC) algorithms can be sensitive to model mismatch when used in challenging nonlinear control tasks. In particular, the performance of MPC for vehicle control at the limits of handling suffers when the underlying model overestimates the vehicle's capabilities. In this work, we propose a risk-averse MPC framework that explicitly accounts for uncertainty over friction limits and tire parameters. Our approach leverages a sample-based approximation of an optimal control problem with a conditional value at risk (CVaR) constraint. This sample-based formulation enables planning with a set of expressive vehicle dynamics models using different tire parameters. Moreover, this formulation enables efficient numerical resolution via sequential quadratic programming and GPU parallelization. Experiments on a Lexus LC 500 show that risk-averse MPC unlocks reliable performance, while a deterministic baseline that plans using a single dynamics model may lose control of the vehicle in adverse road conditions.

Entropy or randomness represents a foundational security property in security-related operations, such as key generation. Key generation in turn is central to security protocols such as authentication and encryption. Physical unclonable functions (PUF) are hardware-based primitives that can serve as key generation engines in modern microelectronic devices and applications. PUFs derive entropy from manufacturing variations that exist naturally within and across otherwise identical copies of a device. However, the levels of random variations that represent entropy, which are strongly correlated to the quality of the PUF-generated bitstrings, vary from one manufacturer to another. In this paper, we evaluate entropy across a set of devices manufactured by three mainstream FPGA vendors, Xilinx, Altera and Microsemi. The devices selected for evaluation are considered low-end commercial devices to make the analysis relevant to IoT applications. The SiRF PUF is used in the evaluation, and is constructed nearly identically across the three vendor devices, setting aside minor differences that exist in certain logic element primitives used within the PUF architecture, and which have only a minor impact on our comparative analysis. The SiRF PUF uses a high-resolution time-to-digital converter (TDC) crafted from high-speed carry-chain logic embedded within each device to measure path delays in an engineered netlist of logic gates as a source of entropy. Therefore, our analysis includes an evaluation of actual path delay variation as it exists across the three device classes, as well as a statistical evaluation of the PUF-generated bitstrings. A reliablity analysis is also provided using data collected in industrial-standard temperature experiments to round out the evaluation of important statistical properties of the PUF., Comment: 15 pages, 22 figures, IEEE journal

In this study, we investigate in situ etching of \b{eta}-Ga2O3 in a metal-organic chemical vapor deposition (MOCVD) system using tert-Butyl chloride (TBCl). We report the successful etching of both heteroepitaxial (-201)-oriented and homoepitaxial (010)-oriented \b{eta}-Ga2O3 films over a wide range of substrate temperature, TBCl molar flows, and reactor pressures. We identify that the likely etchant is HCl (g) formed by the pyrolysis of TBCl in the hydrodynamic boundary layer above the substrate. The temperature dependence of the etch rate reveals two distinct regimes characterized by markedly different apparent activation energies. The extracted apparent activation energies suggest that at temperatures below ~800 {\deg}C the etch rate is likely limited by desorption of etch products, while at higher substrate temperatures chemisorption of HCl limits the etch rate. The relative etch rates of heteroepitaxial (-201) and homoepitaxial (010) \b{eta}-Ga2O3 were observed to scale by the ratio of the surface energies indicating an anisotropic etch. For (010) homoepitaxial films, relatively smooth post-etch surface morphology was achieved by tuning the etching parameters., Comment: 12 pages, 8 figures

Here we report that the source of Si impurities commonly observed on (010) $\beta-Ga_2O_3$ is from exposure of the surface to air. Moreover, we find that a 15 minute HF (49%) treatment reduces the Si density by approximately 1 order of magnitude on (010) $\beta-Ga_2O_3$ surfaces. This reduction in Si is critical for the elimination of the often observed parasitic conducting channel, which negatively affects transport properties and lateral transistor performance. After the HF treatment the sample must be immediately put under vacuum, for the Si fully returns within 10 minutes of additional air exposure. Lastly, we demonstrate that performing a 30 minute HF (49%) treatment on the substrate before growth has no deleterious effect on the structure or on the epitaxy surface after subsequent $Ga_2O_3$ growth.

Fabrication of superconducting nanomechanical resonators for quantum research, detectors and devices traditionally relies on a lithographic process, resulting in oscillators with sharp edges and a suspended length limited to a few 100 micrometres. We report a low-investment top-down approach to fabricating NbTi nanowire resonators with suspended lengths up to several millimetres and diameters down to 100 nanometres. The nanowires possess high critical currents and fields, making them a natural choice for magnetomotive actuation and sensing. This fabrication technique is independent of the substrate material, dimensions and layout and can readily be adapted to fabricate nanowire resonators from any metal or alloy with suitable ductility and yield strength. Our work thus opens access to a new class of nanomechanical devices with applications including microscopic and mesoscopic investigations of quantum fluids, detecting dark matter and fundamental materials research in one-dimensional superconductors in vacuum., Comment: 19 pages, 4 figures