LARG, Language-based Automatic Reward and Goal Generation
- URL: http://arxiv.org/abs/2306.10985v1
- Date: Mon, 19 Jun 2023 14:52:39 GMT
- Title: LARG, Language-based Automatic Reward and Goal Generation
- Authors: Julien Perez and Denys Proux and Claude Roux and Michael Niemaz
- Abstract summary: We develop 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.
- Score: 8.404316955848602
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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