My research goal is to enable complex robotic systems to learn complex tasks. Thus, I am interested in developing a scalable robot learning framework that can leverage prior knowledge and diverse data.
Particularly, as the first milestone, I focus on solving a long-horizon manipulation task, Furniture Assembly, which requires many aspects of intelligent robots from structural reasoning to long-term planning to sophisticated control.
Key papers: IKEA Environment, T-STAR, SPiRL, SkiMo, Transition-RL
Long-horizon task benchmark | IKEA Environment (2019) |
Skill composition | Transition-RL (2019), Skill-Coordination (2020), T-STAR (2021) |
Learning with skills and skill prior | SPiRL (2020), SkiLD (2021), SkiMo (2022) |
Imitation learning | SILO (2019), Goal Proximity IL (2021) |
Safe exploration | MoPA-RL (2020) |
Simulation-to-real | IDAPT (2021), MoPA-PD (2021) |