J. Daniel Griffith — accepted AI-conference papers

2024

Clio: Real-Time Task-Driven Open-Set 3D Scene Graphs

Dominic Maggio, Yun Chang, Nathan Hughes, Matthew Trang, J. Daniel Griffith, Carlyn Dougherty +3

RA-L 2024

Modern tools for class-agnostic image segmentation (e.g., SegmentAnything) and open-set semantic understanding (e.g., CLIP) provide unprecedented opportunities for robot perception and mapping. While traditional closed-set metric-semantic maps were restricted to tens or hundreds of semantic classes,

Cited by 94 Source ↗code ↗Scholar ↗

2022

Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networks

Zachary Ravichandran, Lisa Peng, Nathan Hughes, J. Daniel Griffith, Luca Carlone

ICRA 2022poster

Representations are crucial for a robot to learn effective navigation policies. Recent work has shown that mid-level perceptual abstractions, such as depth estimates or 2D semantic segmentation, lead to more effective policies when provided as observations in place of raw sensor data (e.g., RGB imag…

Cited by 88 Source ↗code ↗Scholar ↗