activity
20132023
most citedCollision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning

240 citations · 1.2k across the 114 of their papers we have counts for

collaborators

132 papers

cs.RO2023

Wide-Area Geolocalization with a Limited Field of View Camera in Challenging Urban Environments

Lena M. Downes, Ted J. Steiner, Rebecca L. Russell +1

Cross-view geolocalization, a supplement or replacement for GPS, localizes an agent within a search area by matching ground-view images to overhead images. Significant progress has…

cs.LG2023★ 1 cited

RAYEN: Imposition of Hard Convex Constraints on Neural Networks

Jesus Tordesillas, Victor Klemm, Jonathan P. How +1

Despite the numerous applications of convex constraints in Robotics, enforcing them within learning-based frameworks remains an open challenge. Existing techniques either fail to g…

cs.RO2023★ 16 cited

Principles and Guidelines for Evaluating Social Robot Navigation Algorithms

Anthony Francis, Claudia Pérez-D'Arpino, Chengshu Li +28

A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation ha…

cs.RO2023

Efficient Deep Learning of Robust Policies from MPC using Imitation and Tube-Guided Data Augmentation

Andrea Tagliabue, Jonathan P. How

Imitation Learning (IL) can generate computationally efficient policies from demonstrations provided by Model Predictive Control (MPC). However, IL methods often require extensive…

cs.RO2023★ 2 cited

Resilient and Distributed Multi-Robot Visual SLAM: Datasets, Experiments, and Lessons Learned

Yulun Tian, Yun Chang, Long Quang +4

This paper revisits Kimera-Multi, a distributed multi-robot Simultaneous Localization and Mapping (SLAM) system, towards the goal of deployment in the real world. In particular, th…

cs.RO2023

MOTLEE: Distributed Mobile Multi-Object Tracking with Localization Error Elimination

Mason B. Peterson, Parker C. Lusk, Jonathan P. How

We present MOTLEE, a distributed mobile multi-object tracking algorithm that enables a team of robots to collaboratively track moving objects in the presence of localization error.…