5 accepted papers
utonomous racing pushes vehicles to their physical limits, requiring control policies that can rapidly adapt to localized changes in track conditions, such as varying surface friction. Current Reinforcement Learning (RL) approaches rely either on ground-truth system identification, which is impracti…
In model-based control, dynamics models are typically trained by minimizing open-loop prediction errors uniformly across all states. However, due to finite model capacity, this misallocates representational power, as not all prediction errors impact the downstream closed-loop performance equally. In…
Reliable Global Navigation Satellite System (GNSS) signals are increasingly denied or jammed in real-world applications, such as search and rescue operations. In such scenarios, Unmanned Aerial Vehicles (UAVs) must rely on downward-facing cameras for absolute localization against reference satellite…
Model Predictive Control (MPC) is among the most widely adopted and reliable methods for robot control, relying critically on an accurate dynamics model. However, existing dynamics models used in the gradient-based MPC are limited by computational complexity and state representation. To address this…
Velocity estimation is of great importance in autonomous racing. Still, existing solutions are characterized by limited accuracy, especially in the case of aggressive driving or poor generalization to unseen road conditions. To address these issues, we propose to utilize Unscented Kalman Filter (UKF…