mlsanityData to 7 Oct 2026

ManeuverNet: A Soft Actor-Critic Framework for Precise Maneuvering of Double-Ackermann-Steering Robots with Optimized Reward Functions

arXiv 2602.14726 ↗First posted 16 Feb 2026cs.RO cs.AIcited by 1

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Abstract (arXiv)

Autonomous control of double-Ackermann-steering robots is essential in agricultural applications, where robots must execute precise and complex maneuvers within a limited space. Classical methods, such as the Timed Elastic Band (TEB) planner, can address this problem, but they rely on parameter tuning, making them highly sensitive to changes in robot configuration or environment and impractical to deploy without constant recalibration. At the same time, end-to-end deep reinforcement learning (DRL) methods often fail due to unsuitable reward functions for non-holonomic constraints, resulting in sub-optimal policies and poor generalization. To address these challenges, this paper presents ManeuverNet, a DRL framework tailored for double-Ackermann systems, combining Soft Actor-Critic with CrossQ. Furthermore, ManeuverNet introduces four specifically designed reward functions to support maneuver learning. Unlike prior work, ManeuverNet does not depend on expert data or handcrafted guidance. We extensively evaluate ManeuverNet against both state-of-the-art DRL baselines and the TEB planner. Experimental results demonstrate that our framework substantially improves maneuverability and success rates, achieving more than a 40% gain over DRL baselines. Moreover, ManeuverNet effectively mitigates the strong parameter sensitivity observed in the TEB planner. In real-world trials, ManeuverNet achieved up to a 90% increase in maneuvering trajectory efficiency, highlighting its robustness and practical applicability.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 35 references that are in the map. First posted on arXiv 16 Feb 2026.

Builds on

17 of 35 references.

References · 17

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2015Continuous control with deep reinforcement learningcited by 3,279
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  4. 2017Hindsight Experience Replaycited by 1,096
  5. 2018On Evaluation of Embodied Navigation Agentscited by 526
  6. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 388
  7. 2022Goal-Conditioned Reinforcement Learning: Problems and Solutionscited by 145
  8. 2019CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicitycited by 89
  9. 2023FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicingcited by 13
  10. 2022Generalization in Deep Reinforcement Learning for Robotic Navigation by Reward Shapingcited by 8
  11. 2024FRASA: An End-to-End Reinforcement Learning Agent for Fall Recovery and Stand Up of Humanoid Robotscited by 8
  12. 2021Deep Reinforcement Learning for Continuous Docking Control of Autonomous Underwater Vehicles: A Benchmarking Studycited by 6
5 more
  1. 2024Terrain-Attentive Learning for Efficient 6-DoF Kinodynamic Modeling on Vertically Challenging Terraincited by 5
  2. 2022Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogisticscited by 3
  3. 2023Multi Actor-Critic DDPG for Robot Action Space Decomposition: A Framework to Control Large 3D Deformation of Soft Linear Objectscited by 3
  4. 2024FootstepNet: an Efficient Actor-Critic Method for Fast On-line Bipedal Footstep Planning and Forecastingcited by 3
  5. 2025Towards Safe Maneuvering of Double-Ackermann-Steering Robots with a Soft Actor-Critic Frameworkcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026DRL-Based Pose Control for Double-Ackermann Robots Under Actuation Uncertaintiescited by 0