39 citations · 88 across the 8 of their papers we have counts for
3 papers · 1 filter
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…
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…
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…