DRL-Based Pose Control for Double-Ackermann Robots Under Actuation Uncertainties 事件

DRL-Based Pose Control for Double-Ackermann Robots Under Actuation Uncertainties arXiv:2606.00313v1 Announce Type: cross Abstract: Robust deployment of deep reinforcement learning (DRL) policies on real robots remains challenging due to discrepancies between simulation and real-world dynamics. We address this issue in the context of maneuvering with double-Ackermann-steering mobile robots, which introduce additional constraints due to their non-holonomic nature. Building upon the DRL framework