Pei-Zhi Tang

Efficient Online Robotic Reinforcement Learning With Explicit Manifold Alignment

Online reinforcement learning (RL) enables robots to continuously improve through real-world interaction, yet achieving high success rates in contact-rich manipulation remains challenging due to sparse binary rewards. Conventional reward-shaping methods rely on hand-designed goal-proximity heuristics and seldom differe...