1st RSS Workshop on Social Robot Navigation

Event to be held in conjunction with Robotics: Science and Systems 2020 (RSS 2020)



Update (April 6th)
We decided that a virtual organization would take away too much from the highly interactive, discussion-driven workshop that we envisioned. For this reason, we have decided to move our workshop to a future venue to be announced in the next few months. Thank you for your interest in our workshop and for you understanding in this difficult situation.

The social navigation and prediction community has reached a crucial point. Despite the large volume of publications, we lack a “common language.” Broadly speaking, we have yet to reach consensus about:

Finally, social robot navigation and prediction evaluation studies often lack the statistical rigor seen in other research communities (e.g., experimental psychology).

Flea Market Crowd "Flea Market Crowd" by caribb is licensed under CC BY-NC-ND 2.0

Motivated by these observations, we would like to call a diverse, multidisciplinary audience to participate in an interactive, discussion-oriented workshop. We would like to hear from roboticists, social scientists, and designers about how to develop best practices for social robot navigation research. The workshop will be organized around focus topics, which are more narrow “common language” challenges. After our invited speakers briefly present their perspectives upon a focus topic, workshop participants will break off into small, interdisciplinary brainstorming workgroups. These workgroups will be comprised of the invited speakers, the organizers, and the workshop attendees. Ultimately, we want the community to drive the conversation around the focus topics.

Focus topic 1. What are the most important variables that influence social robot navigation performance? In other words, what levers should we be exploring?

Focus topic 2: What are the best abstractions for social robot navigation from the perspective of perception, planning, and control?

Focus topic 3: What properties should a social navigation dataset capture? How do we deal with uncontrollable variables in experimental settings?



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