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

Mobility in an effective and socially-compliant manner is an essential yetchallenging task for robots operating in crowded spaces. Recent works haveshown the power of deep reinforcement learning techniques to learn sociallycooperative policies. However, their cooperation ability deteriorates as thecrowd grows since they typically relax the problem as a one-way Human-Robotinteraction problem. In this work, we want to go beyond first-order Human-Robotinteraction and more explicitly model Crowd-Robot Interaction (CRI). We proposeto (i) rethink pairwise interactions with a self-attention mechanism, and (ii)jointly model Human-Robot as well as Human-Human interactions in the deepreinforcement learning framework. Our model captures the Human-Humaninteractions occurring in dense crowds that indirectly affects the robot'santicipation capability. Our proposed attentive pooling mechanism learns thecollective importance of neighboring humans with respect to their futurestates. Various experiments demonstrate that our model can anticipate humandynamics and navigate in crowds with time efficiency, outperformingstate-of-the-art methods.

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning