6 papers
HOTICE: Whole-Body Humanoid Object Transportation in Cluttered Environments
Toan Nguyen, Weiduo Yuan, Siheng Zhao +2
Object transportation is a fundamental capability for humanoid robots operating in real-world, human-centric environments, yet existing methods struggle when clutter constrains fre…
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…
ICLR: In-Context Imitation Learning with Visual Reasoning
Toan Nguyen, Weiduo Yuan, Songlin Wei +3
In-context imitation learning enables robots to adapt to new tasks from a small number of demonstrations without additional training. However, existing approaches typically conditi…
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…