1st RSS Workshop on Social Robot Navigation

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

Call for Papers

We invite short papers (max 4 pages, excluding citations, in standard RSS paper format, but not anonymized) on topics related to social robot navigation or social trajectory prediction. Papers may be submitted in PDF format via email to the address socialrobotnavigation@gmail.com by April 9th (anywhere on earth). All accepted papers will be presented as posters and introduced via lightning talks. The papers are encouraged (but are not required to) address the workshop’s focus topics and the following themes of interest.

Themes of Interest

Design and experiments
-What robot behaviors will enable untrained pedestrians to navigate effectively around robots?
-How do we design user studies to address and get beyond novelty effects?
-How do we measure the performance of a social robot navigation system?
-How do we deal with uncontrollable variables in experimental settings?
-What are appropriate benchmarking experiments and baselines?
-How do we ensure these benchmarks are accessible to the community?
-What is the role of simulation in social robot navigation research?
-What aspects of human behavior can and should be simulated?

Social robot navigation formalisms
-What are appropriate representations for capturing important properties of crowd behavior?
-What are appropriate context models?
-What is the right level of context detail and how should it change depending on experimental settings?
-How do we encode behavior specifications into objective or reward functions?
-Should robots try to “mimic” human behavior?
-What behavior models and interaction metaphors are most effective for enabling smooth robot navigation?
-How do we verify that a robot navigation framework is correct and safe?
-How do we manage and deploy a safety-critical system without safety guarantees?

Datasets and data-driven approaches
-What properties and variables should a social navigation dataset capture?
-What is the role of human navigation datasets in social robot navigation research?
-How do we verify that we have not learned dataset-specific artifacts?
-How do we account for “distribution shift” in social navigation settings?
-What is the role of learning (e.g., deep, imitation) in social robot navigation?
-How do simulation datasets limit learning-based approaches?
-How can we ensure smooth transfer to real-world settings?
-Can we extract performance boundaries in sim2real transfer?