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

Dexterous in-hand manipulation for a multi-fingered anthropomorphic hand isextremely difficult because of the high-dimensional state and action spaces,rich contact patterns between the fingers and objects. Even though deepreinforcement learning has made moderate progress and demonstrated its strongpotential for manipulation, it is still faced with certain challenges, such aslarge-scale data collection and high sample complexity. Especially, for someslight change scenes, it always needs to re-collect vast amounts of data andcarry out numerous iterations of fine-tuning. Remarkably, humans can quicklytransfer learned manipulation skills to different scenarios with littlesupervision. Inspired by human flexible transfer learning capability, wepropose a novel dexterous in-hand manipulation progressive transfer learningframework (PTL) based on efficiently utilizing the collected trajectories andthe source-trained dynamics model. This framework adopts progressive neuralnetworks for dynamics model transfer learning on samples selected by a newsamples selection method based on dynamics properties, rewards and scores ofthe trajectories. Experimental results on contact-rich anthropomorphic handmanipulation tasks show that our method can efficiently and effectively learnin-hand manipulation skills with a few online attempts and adjustment learningunder the new scene. Compared to learning from scratch, our method can reducetraining time costs by 95%.

Progressive Transfer Learning for Dexterous In-Hand Manipulation with Multi-Fingered Anthropomorphic Hand