Haolin Wu

ASCENT: Autonomous Skill Learning Toward Complex Embodied Tasks With Foundation Models

Collecting data from simulated scenarios for training robotic skills provides a safer and more controllable alternative to real-world environments. However, it demands considerable effort, including the manual construction of simulation environments, the careful design of tasks, and the challenge of