13 accepted papers
Safe and precise maneuvering of quadrotor unmanned aerial vehicles (UAVs) in high-speed wind environments remains a critical challenge. Wind disturbances are nonlinear, time-varying, and difficult to model, causing traditional controllers to struggle with perception and compensation, especially unde
Semantic segmentation is a fundamental task in computer vision with wide-ranging applications, including autonomous driving and robotics. While RGB-based methods have achieved strong performance with CNNs and Transformers, their effectiveness degrades under fast motion, low-light, or high dynamic ra
Reliable quadrotor control in dynamic environments remains challenging due to external disturbances and internal uncertainties. While Model Predictive Path Integral (MPPI) control enables agile maneuvers through samplingbased optimization, its performance often degrades under such unmodeled uncertai…
Mechanical ventilation (MV) is essential in intensive care units (ICUs), yet conventional protocols lack personalization and risk harmful over- or under-ventilation. Offline reinforcement learning (ORL) enables policy optimization from retrospective clinical data without unsafe online interaction, b
Unmanned Aerial Vehicles (UAVs) play an important role in various applications, where precise trajectory tracking is crucial. However, conventional control algorithms for trajectory tracking often exhibit limited performance due to the underactuated, nonlinear, and highly coupled dynamics of quadrot
In recent years, affordance detection has become essential for robotic manipulation in real-world scenes, where robots must autonomously interpret commands and perform actions. Current methods often focus on individual point cloud objects or simple semantic queries, limiting their effectiveness in d…
Generating layouts from textual descriptions by large language models (LLMs) plays a crucial role in precise spatial reasoning-induced domains such as robotic object rearrangement and text-to-image generation. However, current methods face challenges in limited real-world examples, handling diverse…
Event-based semantic segmentation has great potential in autonomous driving and robotics due to the advantages of event cameras, such as high dynamic range, low latency, and low power cost. Unfortunately, current artificial neural network (ANN)-based segmentation methods suffer from high computation
In recent years, wide-area visual surveillance systems have been widely applied in various industrial and transportation scenarios. These systems, however, face significant challenges when implementing multi-object detection due to conflicts arising from the need for high-resolution imaging, efficie…
Visual odometry is important for plenty of applications such as autonomous vehicles, and robot navigation. It is challenging to conduct visual odometry in textureless scenes or environments with sudden illumination changes where popular feature-based methods or direct methods cannot work well. To ad…
Event-based sensors (e.g., DVS cameras) are capable of higher dynamic range, higher temporal resolution, lower time latency, and better power efficiency compared to conventional devices (e.g., RGB cameras). However, learning from these sensors remains challenging; event-based sensors output a stream
The advantages of event-sensing over conventional sensors (e.g., higher dynamic range, lower time latency, and lower power consumption) have spurred research into machine learning for event data. Unsurprisingly, deep learning has emerged as a competitive methodology for learning with event sensors;…
Tactile perception is crucial for a variety of robot tasks including grasping and in-hand manipulation. New advances in flexible, event-driven, electronic skins may soon endow robots with touch perception capabilities similar to humans. These electronic skins respond asynchronously to changes (e.g.,…