CALoRA: Content-Aware Low-Rank Adaptation for UAV Transfer Learning

Conventional Low-Rank Adaptation (LoRA) constrains weight updates to a static linear low-rank manifold, which is inherently limited when applied to Reinforcement Learning (RL) tasks for Unmanned Aerial Vehicle (UAV) applications. UAVs operate in highly dynamic and nonstationary environments where rapid variations in sensing and state transitions lead to complex, nonlinear input–output relationships. Such environmental complexity cannot be adequately modeled by a static Low-rank approximation, making conventional LoRA approaches insufficient for the high-dimensional dynamics required in UAV applications. To overcome these limitations, we propose an attention-enhanced LoRA that constructs an input-dependent and intrinsically nonlinear adaptation manifold. By integrating a nonstandard attention mechanism into the vanilla LoRA, our method enables the model to dynamically reshape its weight subspace in response to changing environmental conditions. This allows the policy and value networks to capture diverse local patterns as well as global contextual structure during adaptation, ultimately improving robustness under domain shift and nonstationary data distributions. We evaluate the proposed method in UAV adaptation scenario based on the AirSim simulator, where a multi-agent training is conducted with internally collected datasets, including multi-sensor observations and UAV physical state information, and policies are transferred from obstacle-free to cluttered environments. Compared to vanilla LoRA, the proposed method reduces initial reward variance by over 70%, leading to earlier adaptation and more stable generalization, and exhibits richer nonlinear expressive power, allowing the model to accommodate the complex, high-dimensional characteristic of UAV tasks.