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

Urban search and rescue missions require rapid first response to minimizeloss of life and damage. Often, such efforts are assisted by humanitarianrobots which need to handle dynamic operational conditions such as uneven andrough terrains, especially during mass casualty incidents like an earthquake.Quadruped robots, owing to their versatile design, have the potential to assistin such scenarios. However, control of quadruped robots in dynamic and roughterrain environments is a challenging problem due to the many degrees offreedom of these robots. Current locomotion controllers for quadrupeds arelimited in their ability to produce multiple adaptive gaits, solve tasks in atime and resource-efficient manner, and require tedious training and manualtuning procedures. To address these challenges, we propose MTAC: a multi-gaitterrain-adaptive controller, which utilizes a Hierarchical reinforcementlearning (HRL) approach while being time and memory-efficient. We show that ourproposed method scales well to a diverse range of environments with similarcompute times as state-of-the-art methods. Our method showed greater than 75%on most tasks, outperforming previous work on the majority of test cases.

MTAC: Hierarchical Reinforcement Learning-based Multi-gait Terrain-adaptive Quadruped Controller