Publications (14)
Recurrent Reinforcement Learning with Memoroids
Steven Morad, Chris Lu, Ryan Kortvelesy +3
Memory models such as Recurrent Neural Networks (RNNs) and Transformers address Partially Observable Markov Decision Processes (POMDPs) by mapping trajectories to latent Markov sta…
Reinforcement Learning with Fast and Forgetful Memory
Steven Morad, Ryan Kortvelesy, Stephan Liwicki +1
Nearly all real world tasks are inherently partially observable, necessitating the use of memory in Reinforcement Learning (RL). Most model-free approaches summarize the trajectory…
ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time
Rudra P K Poudel, Ujwal Bonde, Stephan Liwicki +1
Modern deep learning architectures produce highly accurate results on many challenging semantic segmentation datasets. State-of-the-art methods are, however, not directly transfera…
DiaLoc: An Iterative Approach to Embodied Dialog Localization
Chao Zhang, Mohan Li, Ignas Budvytis +1
Multimodal learning has advanced the performance for many vision-language tasks. However, most existing works in embodied dialog research focus on navigation and leave the localiza…
Embodied Visual Navigation with Automatic Curriculum Learning in Real Environments
Steven D. Morad, Roberto Mecca, Rudra P. K. Poudel +2
We present NavACL, a method of automatic curriculum learning tailored to the navigation task. NavACL is simple to train and efficiently selects relevant tasks using geometric featu…
ReCoRe: Regularized Contrastive Representation Learning of World Model
Rudra P. K. Poudel, Harit Pandya, Stephan Liwicki +1
While recent model-free Reinforcement Learning (RL) methods have demonstrated human-level effectiveness in gaming environments, their success in everyday tasks like visual navigati…