用强化学习让双阿克曼机器人在不确定执行下精准控姿，实现实时部署。
DRL-Based Pose Control for Double-Ackermann Robots Under Actuation Uncertainties
- 扩展原框架，从位置控制升级为完整姿态控制，提升任务难度。
- 简化执行模型导致仿真到真实迁移失败，成功率达25%；引入真实执行效应后提升至92%。
- 采用模拟-模拟-真实策略，无需额外调参即可在真实机器人上稳定运行。
深度强化学习（DRL）在真实机器人上的鲁棒部署仍面临仿真与现实动态差异的挑战。本文针对双阿克曼转向移动机器人在非完整约束下的路径规划问题，基于ManeuverNet框架将控制目标从位置控制扩展为全姿态控制，显著提高任务复杂度。研究发现，训练时使用简化的执行模型会导致策略泛化能力下降：在更严苛评估条件下，成功率从PyBullet中的100%降至Gazebo中的25%。为此，提出sim-to-sim-to-real方法，将Gazebo中观测到的执行误差引入PyBullet训练环境。结合SAC与CrossQ的多环境DRL算法，所学策略对建模误差具有强鲁棒性，使Gazebo中成功率提升至92%，严格阈值下仍保持69%。最终无需额外调参即成功部署于真实机器人。
原文摘要 · Abstract (English)
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 ManeuverNet, we extend its objective from position control to full pose control, resulting in a more challenging task. We further investigate the impact of actuation-related uncertainties on policy transfer. The use of simplified actuation models during training of the extended policy can lead to poor generalization, shown by a success rate drop from 100% in PyBullet to 25% in Gazebo under stricter evaluation conditions. To address this limitation, we adopt a sim-to-sim-to-real approach, where actuation effects observed in Gazebo are incorporated into the PyBullet training environment. Using multi-environment DRL with SAC and CrossQ, we learn policies that remain robust despite modeling inaccuracies. This approach can significantly reduce the performance gap across simulators, achieving up to 92% success rate in Gazebo and maintaining 69% under stricter thresholds, with successful transfer to a real robot without additional tuning.
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