3 citations · 6 across the 5 of their papers we have counts for
6 papers
Defensive Escort Teams via Multi-Agent Deep Reinforcement Learning
Arpit Garg, Yazied A. Hasan, Adam Yañez +1
Coordinated defensive escorts can aid a navigating payload by positioning themselves in order to maintain the safety of the payload from obstacles. In this paper, we present a nove…
RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policies
Hao-Tien Lewis Chiang, Jasmine Hsu, Marek Fiser +2
This paper addresses two challenges facing sampling-based kinodynamic motion planning: a way to identify good candidate states for local transitions and the subsequent computationa…
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…
PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning
Aleksandra Faust, Oscar Ramirez, Marek Fiser +4
We present PRM-RL, a hierarchical method for long-range navigation task completion that combines sampling based path planning with reinforcement learning (RL). The RL agents learn…