Abstract-- Autonomous docking between Unmanned Aerial V ehicles (UA Vs) and ground robots is essential for heterogeneous systems, yet most existing approaches target wheeled platforms whose limited mobility constrains exploration in complex terrains. Quadruped robots offer superior adaptability but undergo frequent posture variations, making it difficult to provide a stable landing surface for UA Vs. T o address these challenges, we propose an autonomous UA V-quadruped docking framework for GPS-denied environments. On the quadruped side, a Hybrid Internal Model with Horizontal Alignment (HIM-HA), learned via deep reinforcement learning, actively stabilizes the torso to provide a level platform. On the UA V side, a three-phase strategy is adopted, consisting of long-range acquisition with a median-filtered YOLOv8 detector, close-range tracking with a constraint-aware controller that integrates a Nonsingular Fast T erminal Sliding Mode Controller (NFTSMC) and a logarithmic Barrier Function (BF) to guarantee finite-time error convergence under field-of-view (FOV) constraints, and terminal descent guided by a Safety Period (SP) mechanism that jointly verifies tracking accuracy and platform stability. I. INTRODUCTION Heterogeneous cooperative systems that integrate Unmanned Aerial V ehicles (UA Vs) and Unmanned Ground V ehicles (UGVs) can expand operational scope and improve efficiency compared to single-domain platforms [1]. Autonomous docking is a key capability for many UA V-UGV collaborative tasks, yet most existing schemes focus on wheeled UGVs, whose mobility is restricted to flat terrain, limiting exploration in complex environments. Moreover, dynamic docking requires UA Vs to achieve precise localization and safe landing on moving platforms, imposing high demands on sensor fusion and robust control [2]. Quadruped robots, with their legged morphology, surpass wheeled and tracked UGVs in unstructured terrains and enable UA V collaboration in challenging environments such as mountains or tunnels.

Developing custom reasoning models via Reinforcement Learning (RL) that can incorporate organization-specific knowledge has great potential to address problems faced by enterprise customers. In many of these problems, the reward function is verifiable, a setting termed RL with Verifiable Rewards (RLVR). We apply RLVR to a popular data science benchmark called BIRD that measures the ability of an AI agent to convert a natural language query for a database to SQL executions. We apply a simple and general-purpose training recipe involving careful prompt and model selection, a warm-up stage using our offline RL approach called TAO, followed by rigorous online RLVR training. With no additional training data beyond the BIRD training set and no use of proprietary models, our very first submission to the BIRD leaderboard reached state-of-the-art accuracy on the private test set: 73.56% without self-consistency and 75.68% with self-consistency. In the latter case, our model also required fewer generations than the second-best approach. While BIRD is only a proxy task, the simplicity of our framework makes it broadly applicable to enterprise domains such as business intelligence, data science, and coding.