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[2602.14726] ManeuverNet: A Soft Actor-Critic Framework for Precise Maneuvering of Double-Ackermann-Steering Robots with Optimized Reward Functions
Summary
ManeuverNet introduces a Soft Actor-Critic framework for enhancing the maneuverability of double-Ackermann-steering robots, addressing limitations of traditional methods and improving efficiency in real-world applications.
Why It Matters
This research is significant as it tackles the challenges of precise maneuvering in robotics, particularly in agricultural settings where space is limited. By improving the robustness and efficiency of maneuvering algorithms, it opens up new possibilities for the deployment of autonomous robots in complex environments.
Key Takeaways
- ManeuverNet combines Soft Actor-Critic with CrossQ for improved robot maneuvering.
- The framework introduces four tailored reward functions that enhance learning without expert data.
- Experimental results show a 40% improvement over existing DRL baselines in maneuverability.
- ManeuverNet reduces parameter sensitivity compared to traditional methods like the TEB planner.
- Real-world trials indicate a 90% increase in trajectory efficiency, demonstrating practical applicability.
Computer Science > Robotics arXiv:2602.14726 (cs) [Submitted on 16 Feb 2026] Title:ManeuverNet: A Soft Actor-Critic Framework for Precise Maneuvering of Double-Ackermann-Steering Robots with Optimized Reward Functions Authors:Kohio Deflesselle, Mélodie Daniel, Aly Magassouba, Miguel Aranda, Olivier Ly View a PDF of the paper titled ManeuverNet: A Soft Actor-Critic Framework for Precise Maneuvering of Double-Ackermann-Steering Robots with Optimized Reward Functions, by Kohio Deflesselle and 4 other authors View PDF HTML (experimental) Abstract: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 do...
