24 citations · 35 across the 7 of their papers we have counts for
7 papers
DeTra: A Unified Model for Object Detection and Trajectory Forecasting
Sergio Casas, Ben Agro, Jiageng Mao +4
The tasks of object detection and trajectory forecasting play a crucial role in understanding the scene for autonomous driving. These tasks are typically executed in a cascading ma…
UnO: Unsupervised Occupancy Fields for Perception and Forecasting
Ben Agro, Quinlan Sykora, Sergio Casas +2
Perceiving the world and forecasting its future state is a critical task for self-driving. Supervised approaches leverage annotated object labels to learn a model of the world -- t…
QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving
Sourav Biswas, Sergio Casas, Quinlan Sykora +3
A self-driving vehicle must understand its environment to determine the appropriate action. Traditional autonomy systems rely on object detection to find the agents in the scene. H…
Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving
Ben Agro, Quinlan Sykora, Sergio Casas +1
A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection…
Toward Globally Optimal State Estimation Using Automatically Tightened Semidefinite Relaxations
Frederike Dümbgen, Connor Holmes, Ben Agro +1
In recent years, semidefinite relaxations of common optimization problems in robotics have attracted growing attention due to their ability to provide globally optimal solutions. I…
Learning to Search in Task and Motion Planning with Streams
Mohamed Khodeir, Ben Agro, Florian Shkurti
Task and motion planning problems in robotics combine symbolic planning over discrete task variables with motion optimization over continuous state and action variables. Recent wor…
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor Navigation
Hugues Thomas, Ben Agro, Mona Gridseth +2
We present a self-supervised learning approach for the semantic segmentation of lidar frames. Our method is used to train a deep point cloud segmentation architecture without any h…