210 citations · 248 across the 7 of their papers we have counts for
7 papers
Contextual Graph Representations for Task-Driven 3D Perception and Planning
Christopher Agia
Recent advances in computer vision facilitate fully automatic extraction of object-centric relational representations from visual-inertial data. These state representations, dubbed…
Points2Plans: From Point Clouds to Long-Horizon Plans with Composable Relational Dynamics
Yixuan Huang, Christopher Agia, Jimmy Wu +2
We present Points2Plans, a framework for composable planning with a relational dynamics model that enables robots to solve long-horizon manipulation tasks from partial-view point c…
Semantic Anomaly Detection with Large Language Models
Amine Elhafsi, Rohan Sinha, Christopher Agia +3
As robots acquire increasingly sophisticated skills and see increasingly complex and varied environments, the threat of an edge case or anomalous failure is ever present. For examp…
Text2Motion: From Natural Language Instructions to Feasible Plans
Kevin Lin, Christopher Agia, Toki Migimatsu +2
We propose Text2Motion, a language-based planning framework enabling robots to solve sequential manipulation tasks that require long-horizon reasoning. Given a natural language ins…
STAP: Sequencing Task-Agnostic Policies
Christopher Agia, Toki Migimatsu, Jiajun Wu +1
Advances in robotic skill acquisition have made it possible to build general-purpose libraries of learned skills for downstream manipulation tasks. However, naively executing these…
TASKOGRAPHY: Evaluating robot task planning over large 3D scene graphs
Christopher Agia, Krishna Murthy Jatavallabhula, Mohamed Khodeir +5
3D scene graphs (3DSGs) are an emerging description; unifying symbolic, topological, and metric scene representations. However, typical 3DSGs contain hundreds of objects and symbol…
S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds
Ran Cheng, Christopher Agia, Yuan Ren +2
With the increasing reliance of self-driving and similar robotic systems on robust 3D vision, the processing of LiDAR scans with deep convolutional neural networks has become a tre…