36 citations · 125 across the 10 of their papers we have counts for
12 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…
Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients
Arbaaz Khan, Clark Zhang, Daniel D. Lee +2
In this paper, we explore using deep reinforcement learning for problems with multiple agents. Most existing methods for deep multi-agent reinforcement learning consider only a sma…
Learning Sample-Efficient Target Reaching for Mobile Robots
Arbaaz Khan, Vijay Kumar, Alejandro Ribeiro
In this paper, we propose a novel architecture and a self-supervised policy gradient algorithm, which employs unsupervised auxiliary tasks to enable a mobile robot to learn how to…
Memory Augmented Control Networks
Arbaaz Khan, Clark Zhang, Nikolay Atanasov +3
Planning problems in partially observable environments cannot be solved directly with convolutional networks and require some form of memory. But, even memory networks with sophist…
End-to-End Navigation in Unknown Environments using Neural Networks
Arbaaz Khan, Clark Zhang, Nikolay Atanasov +3
We investigate how a neural network can learn perception actions loops for navigation in unknown environments. Specifically, we consider how to learn to navigate in environments po…
Neural Network Memory Architectures for Autonomous Robot Navigation
Steven W Chen, Nikolay Atanasov, Arbaaz Khan +3
This paper highlights the significance of including memory structures in neural networks when the latter are used to learn perception-action loops for autonomous robot navigation.…
Robust Monocular Flight in Cluttered Outdoor Environments
Shreyansh Daftry, Sam Zeng, Arbaaz Khan +4
Recently, there have been numerous advances in the development of biologically inspired lightweight Micro Aerial Vehicles (MAVs). While autonomous navigation is fairly straight-for…