Reinforcement Learning Enabled Adaptive Multi-Task Control for Bipedal Soccer Robots
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 ballseek...
| Authors | Yulai Zhang, Yinrong Zhang, Ting Wu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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