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

There has been increasing attention on planning model learning in classicalplanning. Most existing approaches, however, focus on learning planning modelsfrom structured data in symbolic representations. It is often difficult toobtain such structured data in real-world scenarios. Although a number ofapproaches have been developed for learning planning models from fully observedunstructured data (e.g., images), in many scenarios raw observations are oftenincomplete. In this paper, we provide a novel framework, \aType{Recplan}, forlearning a transition model from partially observed raw image traces. Morespecifically, by considering the preceding and subsequent images in a trace, welearn the latent state representations of raw observations and then build atransition model based on such representations. Additionally, we propose aneural-network-based approach to learn a heuristic model that estimates thedistance toward a given goal observation. Based on the learned transition modeland heuristic model, we implement a classical planner for images. We exhibitempirically that our approach is more effective than a state-of-the-artapproach of learning visual planning models in the environment with incompleteobservations.

Learning Visual Planning Models from Partially Observed Images