3 accepted papers
Reinforcement Learning (RL)-based methods have significantly improved the locomotion performance of legged robots. However, these motion policies face significant challenges when deployed in the real world. Robots operating in uncertain environments struggle to adapt to payload variations and extern…
Learning policies for complex humanoid tasks remains both challenging and compelling. Inspired by how infants and athletes rely on external support—such as parental walkers or coach-applied guidance—to acquire skills like walking, dancing, and performing acrobatic flips, we propose A2CF: Adaptive As…
Humanoid robot soccer players face fundamental challenges in achieving stable motion execution and ball trajectory control, particularly under balance constraints during single-leg support phases. In this paper, we introduce MAKP (Multi-mode Accurate Kicking Policy), a novel motion generation-based …