Jon E. Froehlich · PI
Mikey Saugstad · Lead
Minchu Kulkarni · Lead
Michael Duan · Lead
Logan Milandin · Lead
Galen Weld · Lead
Alex Liu · Lead
Kevin Wu · Lead
Poster Proceedings of ASSETS 2024 | Acceptance Rate: 66.7% (58 / 87)
PDF doi Cite Deep Learning for Sidewalk Assessment Project Sidewalk
Proceedings of ASSETS'22 2022 | Acceptance Rate: 58.9% (43 / 73)
PDF doi Cite Deep Learning for Sidewalk Assessment Project Sidewalk
Workshop Proceedings of CVPR2022 Workshop: Accessibility, Vision, and Autonomy (AVA) 2022
PDF arXiv doi Cite Deep Learning for Sidewalk Assessment Project Sidewalk
Proceedings of ASSETS 2019
|
Acceptance Rate: 25.9% (41 / 158)
|
Best Paper Award
PDF doi Cite Code Deep Learning for Sidewalk Assessment Project Sidewalk
Feb 29, 2024 | NSF Smart and Connected Communities Panel on “Pathways to Transitioning Project Outcomes"
Nashville, Tennessee
Jan 22, 2020 | Google Tech Talk
Seattle, WA
Oct 29, 2019 | ASSETS'19
Pittsburgh, PA
![Illustration of a multi-story indoor house layout used in the CapNav benchmark. The diagram asks: “Can [AGENT] go from the basement foyer to the top floor rest area?” The basement foyer (START) is at the bottom left, and the top floor rest area (END) is at the top right. Colored paths show different possible routes through labeled areas including Garage, First Floor Hallway, and Elevator. Icons on the left represent four agent types: a wheelchair user (allowed, green check), a quadruped robot (allowed, green check), a human (allowed, green check), and a sweeping robot (not allowed, red X). Red X markers and warning labels indicate constraints such as “Cannot go up stairs” and “Cannot operate elevator,” showing how different mobility capabilities affect route feasibility.](/media/projects/images/CapNav-Thumbnail.png.500x300_q85_box-135%2C10%2C2826%2C1627_crop_upscale.png)
