This paper introduces a universal and versatile interface for controlling drones using large language models (LLMs) via the new Model Context Protocol (MCP) standard. It enables LLMs to command both real and simulated drones, dynamically integrating real-time situational data like maps for complex missions.

MonoRace is an autonomous drone racing system that utilizes a monocular camera and IMU to achieve champion-level performance, notably winning the A2RL 2025 competition. It features robust state estimation combining neural-network-based gate segmentation with a drone model, an offline optimization procedure, and a neural network for guidance and control.

This paper introduces a novel hot-casted-droplet batch fabrication method for polydimethylsiloxane (PDMS) templates, designed for the robotic transfer of two-dimensional (2D) materials. The method achieves highly uniform templates with ultra-smooth surfaces and precisely controllable thermomechanical responses, crucial for advanced AI-driven material assembly.

This thesis proposes a novel robust deep learning control framework with guaranteed performance for heavy-duty machinery. It addresses challenges in electrification and AI integration by ensuring safety and reliability across diverse actuation mechanisms and operational conditions.

This paper introduces Stage-Aware Reinforcement (StARe), a novel module that decomposes long-horizon robotic manipulation tasks into semantically meaningful stages, providing dense, interpretable reinforcement signals. Integrated into the Imitation → Preference → Interaction (IPI) fine-tuning pipeline, StARe significantly improves the performance and robustness of Vision-Language-Action (VLA) models on complex manipulation tasks.

This paper introduces "Artificial Microsaccade Compensation," a real-time video stabilization method inspired by biological microsaccades. It enables stable camera-based perception for aggressively shaking tailless ornithopters, a significant challenge for autonomous flapping-wing robots.

This paper introduces a modular architecture for autonomous vehicles designed for racing in closed circuits. It integrates perception, localization, path planning, and control subsystems to achieve real-time, precise autonomous navigation in controlled environments.