Jia Pan

Text world models can transform LLM-based agents from reactive responders to proactive planners, fundamentally changing how they interact with complex environments.

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.