Core Challenges of Social Robot Navigation: A Survey

Author:

Mavrogiannis Christoforos1,Baldini Francesca2,Wang Allan3,Zhao Dapeng3,Trautman Pete4,Steinfeld Aaron3,Oh Jean3

Affiliation:

1. Paul G. Allen School of Computer Science & Engineering, University of Washington, WA, USA

2. Honda Research Institute and California Institute of Technology, Pasadena, CA, USA

3. The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA

4. Honda Research Institute, San Jose, CA, USA

Abstract

Robot navigation in crowded public spaces is a complex task that requires addressing a variety of engineering and human factors challenges. These challenges have motivated a great amount of research resulting in important developments for the fields of robotics and human-robot interaction over the past three decades. Despite the significant progress and the massive recent interest, we observe a number of significant remaining challenges that prohibit the seamless deployment of autonomous robots in crowded environments. In this survey article, we organize existing challenges into a set of categories related to broader open problems in robot planning, behavior design, and evaluation methodologies. Within these categories, we review past work and offer directions for future research. Our work builds upon and extends earlier survey efforts by (a) taking a critical perspective and diagnosing fundamental limitations of adopted practices in the field and (b) offering constructive feedback and ideas that could inspire research in the field over the coming decade.

Funder

Honda Research Institute USA, the National Science Foundation

National Institute on Disability, Independent Living, and Rehabilitation Research

U.S. Army Ground Vehicle Systems Center & Software Engineering Institute at Carnegie Mellon University

Air Force Office of Scientific Research

Ministry of Trade, Industry and Energy (MOTIE) and Korea Institute of Advancement of Technology (KIAT) through the International Cooperative R&D program

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Human-Computer Interaction

Reference288 articles.

1. Social Eye Gaze in Human-Robot Interaction: A Review

2. Social LSTM: Human Trajectory Prediction in Crowded Spaces

3. Analyzing Free-standing Conversational Groups

4. Navigation for human-robot interaction tasks

5. Toshitaka Amaoka, Hamid Laga, Suguru Saito, and Masayuki Nakajima. 2009. Personal space modeling for human-computer interaction. In Entertainment Computing (ICEC’09). Springer Berlin, 60–72.

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