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

We are considering the problem of controlling a team of robotic bodyguardsprotecting a VIP from physical assault in the presence of neutral and/oradversarial bystanders. This task is part of a much larger class of problemsinvolving coordinated robot behavior in the presence of humans. This problem ischallenging due to the large number of active entities with different agendas,the need of cooperation between the robots as well as the requirement to takeinto consideration criteria such as social norms and unobtrusiveness inaddition to the main goal of VIP safety. Furthermore, different settings suchas street, public space or red carpet require very different behavior from therobot. We describe how a multi-agent reinforcement learning approach can evolvebehavior policies for teams of robot bodyguards that compare well withhand-engineered approaches. Furthermore, we show that an algorithm inspired byuniversal value function approximators can learn policies that exhibitappropriate, distinct behavior in environments with different requirements.

Emergence of Scenario-Appropriate Collaborative Behaviors for Teams of Robotic Bodyguards