AINeutralarXiv – CS AI · 7h ago6/10
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DRL-Based Pose Control for Double-Ackermann Robots Under Actuation Uncertainties
Researchers extended the ManeuverNet deep reinforcement learning framework to achieve full pose control for double-Ackermann mobile robots while addressing the sim-to-real gap caused by actuation uncertainties. By incorporating Gazebo simulation dynamics into PyBullet training through multi-environment DRL, the team achieved 92% success rates in simulation and 69% under strict conditions, with successful real-world deployment without additional tuning.