3 papers
cs.RO2026
Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own
Weirui Ye, Yunsheng Zhang, Haoyang Weng +6
Reinforcement learning (RL) is a promising approach for solving robotic manipulation tasks. However, it is challenging to apply the RL algorithms directly in the real world. For on…
cs.RO2025
Video2Policy: Scaling up Manipulation Tasks in Simulation through Internet Videos
Weirui Ye, Fangchen Liu, Zheng Ding +3
Simulation offers a promising approach for cheaply scaling training data for generalist policies. To scalably generate data from diverse and realistic tasks, existing algorithms ei…
cs.RO2025
Learning Manipulation Skills through Robot Chain-of-Thought with Sparse Failure Guidance
Kaifeng Zhang, Zhao-Heng Yin, Weirui Ye +1
Defining reward functions for skill learning has been a long-standing challenge in robotics. Recently, vision-language models (VLMs) have shown promise in defining reward signals f…