mlsanityData to 14 Oct 2026

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

arXiv 2604.19104 ↗First posted 21 Apr 2026cs.RO cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 12 of its 28 references that are in the map. First posted on arXiv 21 Apr 2026.

Builds on

12 of 28 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Reinforcement Learning Within the Classical Robotics Stack: A Case Study in Robot Soccer12 Dec
  2. 2024Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control30 Jan

Other references · 10

  1. 2023Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learningcited by 133
  2. 2025Learning Humanoid Standing-up Control across Diverse Posturescited by 46
  3. 2025Embodied AI: From LLMs to World Modelscited by 24
  4. 2023TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Playcited by 20
  5. 2025Agility Meets Stability: Versatile Humanoid Control with Heterogeneous Datacited by 11
  6. 2025SPiDR: A Simple Approach for Zero-Shot Safety in Sim-to-Real Transfercited by 8
  7. 2023Designing a skilled soccer team for RoboCup: exploring skill-set-primitives through reinforcement learningcited by 6
  8. 2025Bracing for Impact: Robust Humanoid Push Recovery and Locomotion with Reduced Order Modelscited by 2
  9. 2024Multi-Agent Coordination for a Partially Observable and Dynamic Robot Soccer Environment with Limited Communicationcited by 1
  10. 2025Multi-Agent Reinforcement Learning and Real-Time Decision-Making in Robotic Soccer for Virtual Environmentscited by 1

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Not cited yet.

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Close in wording, no citation either way

  1. 2026SkillX: Unified Multi-Skill Policy Learning for Humanoid Soccer
  2. 2025Toward Real-World Cooperative and Competitive Soccer with Quadrupedal Robot Teams
  3. 2025Like Playing a Video Game: Spatial-Temporal Optimization of Foot Trajectories for Controlled Football Kicking in Bipedal Robots
  4. 2025Humanoid Whole-Body Badminton via an Annealed Reinforcement Learning Curriculum
  5. 2024Agile and versatile bipedal robot tracking control through reinforcement learning
  6. 2025Learning Agile Striker Skills for Humanoid Soccer Robots from Noisy Sensory Input
  7. 2025Learning Vision-Driven Reactive Soccer Skills for Humanoid Robots
  8. 2025Learning coordinated badminton skills for legged manipulators