2 citations · 3 across the 18 of their papers we have counts for
4 papers · 1 filter
LocoWM: High-Precision Locomotion through World-Model-Guided Residual Adaptation
Zijie Zhao, Shengqian Chen, Xiaoxu Wang +3
High-precision locomotion combines motion-command tracking with precise regulation of task-relevant physical states, enabling robots to interact reliably with their surroundings du…
Empowering Multi-Robot Cooperation via Sequential World Models
Zijie Zhao, Honglei Guo, Shengqian Chen +4
Model-based reinforcement learning (MBRL) has achieved remarkable success in robotics due to its high sample efficiency and planning capability. However, extending MBRL to physical…
TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning
Yuhui Chen, Haoran Li, Zhennan Jiang +2
Developing scalable and generalizable reward engineering for reinforcement learning (RL) is crucial for creating general-purpose agents, especially in the challenging domain of rob…
ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policy
Yuhui Chen, Shuai Tian, Shugao Liu +3
Vision-Language-Action (VLA) models have shown substantial potential in real-world robotic manipulation. However, fine-tuning these models through supervised learning struggles to…