39 citations · 61 across the 19 of their papers we have counts for
5 papers · 1 filter
Large Reward Models: Generalizable Online Robot Reward Generation with Vision-Language Models
Yanru Wu, Weiduo Yuan, Ang Qi +4
Reinforcement Learning (RL) has shown strong potential for improving robotic manipulation policies, yet its practical use remains bottlenecked by the difficulty of specifying rewar…
DreamPlan: Efficient Reinforcement Fine-Tuning of Vision-Language Planners via Video World Models
Emily Yue-Ting Jia, Weiduo Yuan, Tianheng Shi +3
Robotic manipulation requires sophisticated commonsense reasoning, a capability naturally possessed by large-scale Vision-Language Models (VLMs). While VLMs show promise as zero-sh…
: An Open Foundation Model Towards Universal Humanoid Loco-Manipulation
Songlin Wei, Hongyi Jing, Boqian Li +12
We introduce (Psi-Zero), an open foundation model to address challenging humanoid loco-manipulation tasks. While existing approaches often attempt to address this fundamental…
D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping
Haozhe Lou, Mingtong Zhang, Haoran Geng +9
Simulation provides a cost-effective and flexible platform for data generation and policy learning to develop robotic systems. However, bridging the gap between simulation and real…
FuzzingRL: Reinforcement Fuzz-Testing for Revealing VLM Failures
Jiajun Xu, Jiageng Mao, Ang Qi +5
Vision Language Models (VLMs) are prone to errors, and identifying where these errors occur is critical for ensuring the reliability and safety of AI systems. In this paper, we pro…