DRL-Based Pose Control for Double-Ackermann Robots Under Actuation Uncertainties
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...
| Authors | Oussama Zaim, Mélodie Daniel, Aly Magassouba et al. |
|---|---|
| Primary category | cs.RO |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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