English(EN) DRL-Based Pose Control for Double-Ackermann Robots Under Actuation Uncertainties

DRL通过sim-to-sim-to-real策略增强机器人姿态控制

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-02 04:00

研究人员开发了一种深度强化学习（DRL）方法，以改进双阿克曼转向机器人的姿态控制，解决了策略从仿真到实际应用迁移的挑战。他们的方法ManeuverNet被扩展以处理完整的姿态控制，并结合了sim-to-sim-to-real策略来应对驱动不确定性。通过在PyBullet环境中利用Gazebo模拟的驱动效应进行训练，DRL策略在Gazebo中达到了92%的成功率，在实体机器人上达到了69%的成功率，且无需进一步调整。 AI

影响这项研究可以提高DRL在机器人控制系统中鲁棒性和实际应用能力。

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Oussama Zaim, M\'elodie Daniel, Aly Magassouba, Miguel Aranda, Olivier Ly · 2026-06-02 04:00

基于DRL的双转向架机器人驱动不确定性下的姿态控制

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-Ac…

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报道来源 [1]

基于DRL的双转向架机器人驱动不确定性下的姿态控制

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