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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Imitating human demonstrations is a promising approach to endow robots withvarious manipulation capabilities. While recent advances have been made inimitation learning and batch (offline) reinforcement learning, a lack ofopen-source human datasets and reproducible learning methods make assessing thestate of the field difficult. In this paper, we conduct an extensive study ofsix offline learning algorithms for robot manipulation on five simulated andthree real-world multi-stage manipulation tasks of varying complexity, and withdatasets of varying quality. Our study analyzes the most critical challengeswhen learning from offline human data for manipulation. Based on the study, wederive a series of lessons including the sensitivity to different algorithmicdesign choices, the dependence on the quality of the demonstrations, and thevariability based on the stopping criteria due to the different objectives intraining and evaluation. We also highlight opportunities for learning fromhuman datasets, such as the ability to learn proficient policies onchallenging, multi-stage tasks beyond the scope of current reinforcementlearning methods, and the ability to easily scale to natural, real-worldmanipulation scenarios where only raw sensory signals are available. We haveopen-sourced our datasets and all algorithm implementations to facilitatefuture research and fair comparisons in learning from human demonstration data.Codebase, datasets, trained models, and more available athttps://arise-initiative.github.io/robomimic-web/

What Matters in Learning from Offline Human Demonstrations for Robot Manipulation