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

Bagging is an essential skill that humans perform in their daily activities.However, deformable objects, such as bags, are complex for robots tomanipulate. This paper presents an efficient learning-based framework thatenables robots to learn bagging. The novelty of this framework is its abilityto perform bagging without relying on simulations. The learning process isaccomplished through a reinforcement learning algorithm introduced in thiswork, designed to find the best grasping points of the bag based on a set ofcompact state representations. The framework utilizes a set of primitiveactions and represents the task in five states. In our experiments, theframework reaches a 60 % and 80 % of success rate after around three hours oftraining in the real world when starting the bagging task from folded andunfolded, respectively. Finally, we test the trained model with two more bagsof different sizes to evaluate its generalizability.

Learning to bag with a simulation-free reinforcement learning framework for robots