3 citations · 6 across the 5 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…
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)…
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…