Jia Pan

Text world models can transform LLM-based agents from reactive responders into proactive planners, enhancing their performance in complex interactive tasks.

GEAR-VLA achieves a remarkable 90.1% success rate in universal grasping tasks, showcasing its ability to generalize across unseen objects and diverse robot embodiments.

Rapidly prototype and benchmark robotic navigation scenarios using simple YAML configurations, eliminating the coding barrier in simulation.