2024
Exploiting Hybrid Policy in Reinforcement Learning for Interpretable Temporal Logic Manipulation
IROS 2024
Reinforcement Learning (RL) based methods have been increasingly explored for robot learning. However, RL based methods often suffer from low sampling efficiency in the exploration phase, especially for long-horizon manipulation tasks, and generally neglect the semantic information from the task lev