Reinforcement Learning Enabled Adaptive Multi-Task Control for Bipedal Soccer Robots
Summary: arXiv:2604.19104v1 Announce Type: cross
Abstract
Developing bipedal football robots in dynamic combat environments presents challenges related to motion stability and deep coupling of multiple tasks, as well as control switching issues between different states such as upright walking and fall recovery. To address these problems, this paper proposes a modular reinforcement learning (RL) framework for achieving adaptive multi-task control.
Key Features of the Proposed Framework
- Separation of Gait Generation and Complex Actions: The framework combines an open-loop feedforward oscillator with a reinforcement learning-based feedback residual strategy. This effectively separates the generation of basic gaits from complex football actions, enhancing the robot’s overall performance.
- Posture-Driven State Machine: A posture-driven state machine is introduced to switch clearly between the ball-seeking and kicking network (BSKN) and the fall recovery network (FRN). This structural design fundamentally prevents state interference, ensuring that the robot can operate smoothly in various scenarios.
- Efficient Training Strategy: The FRN is trained using a progressive force attenuation curriculum learning strategy. This method allows the robot to learn and adapt to different fall recovery techniques effectively, which is crucial for maintaining operational stability.
Simulation Results
The architecture was verified through Unity simulations of bipedal robots, demonstrating remarkable capabilities in spatial adaptability. The robots reliably found and kicked the ball even in restricted corner scenarios, showcasing their proficiency in navigating complex environments.
Additionally, the robots exhibited rapid autonomous fall recovery, achieving an average recovery time of just 0.715 seconds. This rapid response time is vital for ensuring seamless and stable operation, especially in competitive settings where quick maneuverability is essential.
Conclusion
The modular reinforcement learning framework proposed in this study offers a significant advancement in the development of bipedal soccer robots. By integrating effective strategies for task separation and state management, the research not only addresses the challenges of motion stability and control switching but also enhances the overall performance of robotic systems in multi-task environments.
This work lays the groundwork for future enhancements in robotic soccer technology, paving the way for robots that can perform complex tasks with greater efficiency and reliability.