Human-aware robot navigation: A survey

By Thibault Kruse, Amit Kumar Pandey, Rachid Alami, Alexandra Kirsch. Robotics Auton. Syst., 2013

778 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Social Psychology

View Paper: https://www.semanticscholar.org/paper/590a6eaf684244ffb304a3f1b0e64925075be13e

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

In Social Psychology

Citations in the corpus

Inverse Reinforcement Learning algorithms and features for robot navigation in crowds: An experimental comparison (2014, neutral): “Since we are using a simulator, we have resorted to numeric metrics to approximate them in similar fashion to [1].”
Core Challenges of Social Robot Navigation: A Survey (2021, background): “[127] in that it attempts a more holistic treatment of the problem.”
Artificial cognition for social human-robot interaction: An implementation (2017, methodology): “We briefly mention our human-aware motion and manipulation planner [91,105,92,97,93,95], and finally present two execution controller, the PRS-based Shary [83,89,2] and the event-driven pyRobots [85].”
Deliberation for autonomous robots: A survey (2017, background): “, planning [91], monitoring [169], goal reasoning [213], recognition of actions and plans [129], architectures [127], or human–aware navigation [130].”
Crowd-Robot Interaction: Crowd-Aware Robot Navigation With Attention-Based Deep Reinforcement Learning (2018, background): “1m as the virtual radius of the agent to maintain a comfortable distance to humans for human-aware navigation [9].”
Human motion trajectory prediction: a survey (2019, methodology): “Our taxonomy offers a novel way to structure the growing body of literature, containing the categories proposed by Kruse et al. (2013), Lasota et al. (2017) and Lef ` evre et al. (2014) and extending them with a systematic categorization of contextual cues.”
Human–robot interaction review and challenges on task planning and programming (2016, background): “An extended review in human-aware robot navigation is presented in Kruse et al. (2013) and its outcome is based on identifying the need for improvements in the navigational decision-making methods, related to planning between human and robot tasks.”
Learning socially normative robot navigation behaviors with Bayesian inverse reinforcement learning (2016, methodology): “Other parameters such as Proxemics distances used in the metrics are acquired from accompanying literature [1].”