Agile control of robotic systems often requires anticipating how the environment affects system behavior. For example, a driver must perceive the road ahead to anticipate available friction and plan actions accordingly. Achieving such proactive adaptation within autonomous frameworks remains a challenge, particularly under rapidly changing conditions. Traditional modeling approaches often struggle to capture abrupt variations in system behavior, while adaptive methods are inherently reactive and may adapt too late to ensure safety. We propose a vision-conditioned variational Bayesian last-layer dynamics model that leverages visual context to anticipate changes in the environment. The model first learns nominal vehicle dynamics and is then fine-tuned with feature-wise affine transformations of latent features, enabling context-aware dynamics prediction. The resulting model is integrated into an optimal controller for vehicle racing. We validate our method on a Lexus LC500 racing through water puddles. With vision-conditioning, the system completed all 12 attempted laps under varying conditions. In contrast, all baselines without visual context consistently lost control, demonstrating the importance of proactive dynamics adaptation in high-performance applications.

翻译：机器人系统的敏捷控制通常需要预判环境对系统行为的影响。例如，驾驶员必须感知前方路况以预判可用摩擦力并相应规划动作。在自主框架内实现此类前瞻性适应仍具挑战性，尤其在快速变化条件下。传统建模方法往往难以捕捉系统行为的突变，而自适应方法本质上是反应式的，可能因适应过迟而无法确保安全。我们提出一种视觉条件变分贝叶斯末层动力学模型，该模型利用视觉上下文预判环境变化。该模型首先学习标称车辆动力学，随后通过潜在特征的逐特征仿射变换进行微调，从而实现上下文感知的动力学预测。最终模型被集成至车辆竞速的最优控制器中。我们在雷克萨斯LC500涉水竞速场景中验证了该方法。借助视觉条件机制，该系统在变化条件下完成了全部12圈尝试。相比之下，所有缺乏视觉上下文的基线模型均持续失控，这证明了前瞻性动力学适应在高性能应用中的重要性。