5 citations · 14 across the 4 of their papers we have counts for
11 papers
Large Scale Distributed Collaborative Unlabeled Motion Planning with Graph Policy Gradients
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…
Graph Neural Networks for Motion Planning
Arbaaz Khan, Alejandro Ribeiro, Vijay Kumar +1
This paper investigates the feasibility of using Graph Neural Networks (GNNs) for classical motion planning problems. We propose guiding both continuous and discrete planning algor…
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…