Robotic systems deployed in uncertain and dynamically changing environments often face variations in contact conditions, aerodynamic effects, and external disturbances that challenge reliable control. To remain effective under model-based control, these systems require dynamics models that can adapt to such changes, especially when direct access to complete environmental information is limited. To enable adaptability and facilitate integration with model predictive control, we propose a context-aware dynamics model based on neural ordinary differential equations, which infers environmental factors from state-action histories using a two-phase training procedure. We validate the approach across diverse robotic platforms, including a quadrotor in simulation, as well as a Sphero BOLT robot and a Fanuc manipulator in real-world experiments. The results demonstrate that our method effectively adapts to temporally and spatially varying environmental changes across different tasks. Videos are available at https://youtu.be/PY0sNyF2rqE , and the source code is available at https://github.com/syyu410-yu/context-aware-neural-ode-control.git .

翻译：在不确定且动态变化的环境中部署的机器人系统，常面临接触条件、气动效应和外部干扰的变化，这些因素对可靠控制构成挑战。为了在基于模型的控制下保持有效性，此类系统需要能够适应此类变化的动力学模型，尤其是在直接获取完整环境信息受限的情况下。为实现自适应能力并促进与模型预测控制的集成，我们提出了一种基于神经常微分方程的上下文感知动力学模型，该模型通过两阶段训练过程从状态-动作历史中推断环境因素。我们通过多种机器人平台验证了该方法，包括四旋翼飞行器的仿真模拟，以及Sphero BOLT机器人和Fanuc机械臂的实际实验。结果表明，我们的方法能有效适应不同任务中随时间与空间变化的环境变化。视频见https://youtu.be/PY0sNyF2rqE，源代码见https://github.com/syyu410-yu/context-aware-neural-ode-control.git。