2 papers
cs.RO2025
ReWiND: Language-Guided Rewards Teach Robot Policies without New Demonstrations
Jiahui Zhang, Yusen Luo, Abrar Anwar +5
We introduce ReWiND, a framework for learning robot manipulation tasks solely from language instructions without per-task demonstrations. Standard reinforcement learning (RL) and i…
cs.RO2025
Subtask-Aware Visual Reward Learning from Segmented Demonstrations
Changyeon Kim, Minho Heo, Doohyun Lee +4
Reinforcement Learning (RL) agents have demonstrated their potential across various robotic tasks. However, they still heavily rely on human-engineered reward functions, requiring…