Reinforcement Learning Enabled Adaptive Multi-Task Control for Bipedal Soccer Robots

• URL: http://arxiv.org/abs/2604.19104v1
• Date: Tue, 21 Apr 2026 05:27:43 GMT
• Title: Reinforcement Learning Enabled Adaptive Multi-Task Control for Bipedal Soccer Robots
• Abstract summary: This paper proposes a modular reinforcement learning framework for achieving adaptive multi-task control.<n>A posture-driven statemachine is introduced, clearly switching between the ball and kicking network (BSKN) and the fall recoverynetwork (FRN)
• Score: 1.6824051304328205
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Developing bipedal football robots in dynamiccombat environments presents challenges related to motionstability and deep coupling of multiple tasks, as well ascontrol switching issues between different states such as up-right walking and fall recovery. To address these problems,this paper proposes a modular reinforcement learning (RL)framework for achieving adaptive multi-task control. Firstly,this framework combines an open-loop feedforward oscilla-tor with a reinforcement learning-based feedback residualstrategy, effectively separating the generation of basic gaitsfrom complex football actions. Secondly, a posture-driven statemachine is introduced, clearly switching between the ballseeking and kicking network (BSKN) and the fall recoverynetwork (FRN), fundamentally preventing state interference.The FRN is efficiently trained through a progressive forceattenuation curriculum learning strategy. The architecture wasverified in Unity simulations of bipedal robots, demonstratingexcellent spatial adaptability-reliably finding and kicking theball even in restricted corner scenarios-and rapid autonomousfall recovery (with an average recovery time of 0.715 seconds).This ensures seamless and stable operation in complex multi-task environments.

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