Real-world robots must operate under evolving dynamics caused by changing operating conditions, external disturbances, and unmodeled effects. These may appear as gradual drifts, transient fluctuations, or abrupt shifts, demanding real-time adaptation that is robust to short-term variation yet responsive to lasting change. We propose a framework for modeling the nonlinear dynamics of robotic systems that can be updated in real time from streaming data. The method decouples representation learning from online adaptation, using latent representations learned offline to support online closed-form Bayesian updates. To handle evolving conditions, we introduce a changepoint-aware mechanism with a latent variable inferred from data likelihoods that indicates continuity or shift. When continuity is likely, evidence accumulates to refine predictions; when a shift is detected, past information is tempered to enable rapid re-learning. This maintains calibrated uncertainty and supports probabilistic reasoning about transient, gradual, or structural change. We prove that the adaptive regret of the framework grows only logarithmically in time and linearly with the number of shifts, competitive with an oracle that knows timings of shift. We validate on cartpole simulations and real quadrotor flights with swinging payloads and mid-flight drops, showing improved predictive accuracy, faster recovery, and more accurate closed-loop tracking than relevant baselines.

翻译：现实世界中的机器人必须在因运行条件变化、外部干扰及未建模效应而不断演化的动力学环境中运行。这些变化可能表现为渐进漂移、瞬态波动或突变偏移，要求实时适应机制既能抵抗短期波动，又能对持久变化作出响应。本文提出一种机器人系统非线性动力学建模框架，该框架可通过流式数据实时更新。该方法将表征学习与在线适应解耦，利用离线学习的潜在表征支持在线闭式贝叶斯更新。为处理演化条件，我们引入一种变点感知机制，通过从数据似然推断的潜变量来指示连续性或突变。当连续性较高时，证据不断累积以优化预测；当检测到突变时，通过调节历史信息实现快速重新学习。这保持了校准后的不确定性，并支持对瞬态、渐进或结构性变化的概率推理。我们证明该框架的自适应遗憾仅随时间对数增长，并与突变次数呈线性关系，其性能可与知晓突变时序的预言机模型相竞争。通过在倒立摆仿真和真实四旋翼飞行器（搭载摆动负载及飞行中抛落负载）上的验证，本方法相较于相关基线展现出更优的预测精度、更快的恢复能力及更精确的闭环跟踪性能。