119 citations · 355 across the 60 of their papers we have counts for
10 papers · 1 filter
Foundation Models for Semantic Novelty in Reinforcement Learning
Tarun Gupta, Peter Karkus, Tong Che +2
Effectively exploring the environment is a key challenge in reinforcement learning (RL). We address this challenge by defining a novel intrinsic reward based on a foundation model,…
Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models
Filippos Christianos, Peter Karkus, Boris Ivanovic +2
Reasoning with occluded traffic agents is a significant open challenge for planning for autonomous vehicles. Recent deep learning models have shown impressive results for predictin…
Robust and Controllable Object-Centric Learning through Energy-based Models
Ruixiang Zhang, Tong Che, Boris Ivanovic +4
Humans are remarkably good at understanding and reasoning about complex visual scenes. The capability to decompose low-level observations into discrete objects allows us to build a…
AdvDO: Realistic Adversarial Attacks for Trajectory Prediction
Yulong Cao, Chaowei Xiao, Anima Anandkumar +2
Trajectory prediction is essential for autonomous vehicles (AVs) to plan correct and safe driving behaviors. While many prior works aim to achieve higher prediction accuracy, few s…
Online Learning for Traffic Routing under Unknown Preferences
Devansh Jalota, Karthik Gopalakrishnan, Navid Azizan +2
In transportation networks, users typically choose routes in a decentralized and self-interested manner to minimize their individual travel costs, which, in practice, often results…
Data Sharing and Compression for Cooperative Networked Control
Jiangnan Cheng, Marco Pavone, Sachin Katti +2
Sharing forecasts of network timeseries data, such as cellular or electricity load patterns, can improve independent control applications ranging from traffic scheduling to power g…