用神经微分方程建模机器人动态，自动适应环境变化。
Learning Context-Aware Neural ODE Dynamics for Adaptive Robotic Control
- 基于神经微分方程，从历史状态动作推断环境因素。
- 在仿真与真实机器人上均实现对时空变化环境的自适应控制。
- 适合需要实时调整的复杂动态系统控制任务。
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、空气动力效应和外部扰动的变化，挑战可靠控制。为在基于模型的控制下保持有效性，这些系统需具备能适应此类变化的动力学模型，尤其当无法获取完整环境信息时。为此，我们提出一种基于神经常微分方程的上下文感知动力学模型，通过两阶段训练过程从状态-动作历史中推断环境因素。我们在多种机器人平台上验证该方法，包括模拟中的四旋翼无人机，以及真实世界中的 Sphero BOLT 机器人和 Fanuc 机械臂。结果表明，该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。
原文摘要 · Abstract (English)
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 .
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。