Extending ManeuverNet to pose control and training with SAC and CrossQ across simulators that incorporate observed actuation effects yields policies reaching 92% success in Gazebo and successful real-robot transfer.
ManeuverNet: A Soft Actor- Critic Framework for Precise Maneuvering of Double-Ackermann- Steering Robots with Optimized Reward Functions,
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DRL-Based Pose Control for Double-Ackermann Robots Under Actuation Uncertainties
Extending ManeuverNet to pose control and training with SAC and CrossQ across simulators that incorporate observed actuation effects yields policies reaching 92% success in Gazebo and successful real-robot transfer.