36 citations · 125 across the 10 of their papers we have counts for
4 papers · 1 filter
Graph Policy Gradients for Large Scale Unlabeled Motion Planning with Constraints
Arbaaz Khan, Vijay Kumar, Alejandro Ribeiro
In this paper, we present a learning method to solve the unlabelled motion problem with motion constraints and space constraints in 2D space for a large number of robots. To solve…
Learning Safe Unlabeled Multi-Robot Planning with Motion Constraints
Arbaaz Khan, Chi Zhang, Shuo Li +6
In this paper, we present a learning approach to goal assignment and trajectory planning for unlabeled robots operating in 2D, obstacle-filled workspaces. More specifically, we tac…
Graph Policy Gradients for Large Scale Robot Control
Arbaaz Khan, Ekaterina Tolstaya, Alejandro Ribeiro +1
In this paper, we consider the problem of learning policies to control a large number of homogeneous robots. To this end, we propose a new algorithm we call Graph Policy Gradients…
Sufficiently Accurate Model Learning
Clark Zhang, Arbaaz Khan, Santiago Paternain +1
Modeling how a robot interacts with the environment around it is an important prerequisite for designing control and planning algorithms. In fact, the performance of controllers an…