Nature

Residual dynamics learning for trajectory tracking for multi-rotor aerial vehicles

This paper presents a technique to model the residual dynamics between a high-level planner and a low-level controller by considering reference trajectory tracking in a cluttered environment as an ...
Nature

SINDy-RL for interpretable and efficient model-based reinforcement learning

Deep reinforcement learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in complex environments, such as stabilizing a tokamak fusion reactor or ...