LARG, Language-based Automatic Reward and Goal Generation
Julien Perez Denys Proux Claude Roux Michael Niemaz
Goal-conditioned and Multi-Task Reinforcement Learning (GCRL and MTRL)
address numerous problems related to robot learning, including locomotion,
navigation, and manipulation scenarios. Recent works focusing on
language-defined robotic manipulation tasks have led to the tedious production
of massive human annotations to create dataset of textual descriptions
associated with trajectories. To leverage reinforcement learning with
text-based task descriptions, we need to produce reward functions associated
with individual tasks in a scalable manner. In this paper, we leverage recent
capabilities of Large Language Models (LLMs) and introduce \larg,
Language-based Automatic Reward and Goal Generation, an approach that converts
a text-based task description into its corresponding reward and goal-generation
functions We evaluate our approach for robotic manipulation and demonstrate its
ability to train and execute policies in a scalable manner, without the need
for handcrafted reward functions.