English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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 onlanguage-defined robotic manipulation tasks have led to the tedious productionof massive human annotations to create dataset of textual descriptionsassociated with trajectories. To leverage reinforcement learning withtext-based task descriptions, we need to produce reward functions associatedwith individual tasks in a scalable manner. In this paper, we leverage recentcapabilities of Large Language Models (LLMs) and introduce \larg,Language-based Automatic Reward and Goal Generation, an approach that convertsa text-based task description into its corresponding reward and goal-generationfunctions We evaluate our approach for robotic manipulation and demonstrate itsability to train and execute policies in a scalable manner, without the needfor handcrafted reward functions.

LARG, Language-based Automatic Reward and Goal Generation