154 citations · 449 across the 38 of their papers we have counts for
7 papers · 1 filter
Learning to Navigate the Web
Izzeddin Gur, Ulrich Rueckert, Aleksandra Faust +1
Learning in environments with large state and action spaces, and sparse rewards, can hinder a Reinforcement Learning (RL) agent's learning through trial-and-error. For instance, fo…
PEARL: PrEference Appraisal Reinforcement Learning for Motion Planning
Aleksandra Faust, Hao-Tien Lewis Chiang, Lydia Tapia
Robot motion planning often requires finding trajectories that balance different user intents, or preferences. One of these preferences is usually arrival at the goal, while anothe…
Resilient Computing with Reinforcement Learning on a Dynamical System: Case Study in Sorting
Aleksandra Faust, James B. Aimone, Conrad D. James +1
Robots and autonomous agents often complete goal-based tasks with limited resources, relying on imperfect models and sensor measurements. In particular, reinforcement learning (RL)…
Learning Navigation Behaviors End-to-End with AutoRL
Hao-Tien Lewis Chiang, Aleksandra Faust, Marek Fiser +1
We learn end-to-end point-to-point and path-following navigation behaviors that avoid moving obstacles. These policies receive noisy lidar observations and output robot linear and…
MPDCompress - Matrix Permutation Decomposition Algorithm for Deep Neural Network Compression
Lazar Supic, Rawan Naous, Ranko Sredojevic +2
Deep neural networks (DNNs) have become the state-of-the-art technique for machine learning tasks in various applications. However, due to their size and the computational complexi…
Deep Neural Networks for Swept Volume Prediction Between Configurations
Hao-Tien Lewis Chiang, Aleksandra Faust, Lydia Tapia
Swept Volume (SV), the volume displaced by an object when it is moving along a trajectory, is considered a useful metric for motion planning. First, SV has been used to identify co…