AD-NODE: Adaptive Dynamics Learning with Neural ODEs for Mobile Robots Control
This work addresses the challenge of adaptive control for mobile robots in uncertain environments, such as logistics and agriculture, but it is incremental as it builds on existing neural ODE and model-based control methods.
The paper tackled the problem of enabling mobile robots to adapt to uncertain environments by proposing an adaptive dynamics model that infers operational environments from state-action history, bypassing the need for direct environmental knowledge, and demonstrated its effectiveness in goal-reaching and path-tracking tasks on three robotic platforms with varying conditions.
Mobile robots, such as ground vehicles and quadrotors, are becoming increasingly important in various fields, from logistics to agriculture, where they automate processes in environments that are difficult to access for humans. However, to perform effectively in uncertain environments using model-based controllers, these systems require dynamics models capable of responding to environmental variations, especially when direct access to environmental information is limited. To enable such adaptivity and facilitate integration with model predictive control, we propose an adaptive dynamics model which bypasses the need for direct environmental knowledge by inferring operational environments from state-action history. The dynamics model is based on neural ordinary equations, and a two-phase training procedure is used to learn latent environment representations. We demonstrate the effectiveness of our approach through goal-reaching and path-tracking tasks on three robotic platforms of increasing complexity: a 2D differential wheeled robot with changing wheel contact conditions, a 3D quadrotor in variational wind fields, and the Sphero BOLT robot under two contact conditions for real-world deployment. Empirical results corroborate that our method can handle temporally and spatially varying environmental changes in both simulation and real-world systems.