no code implementations • 11 Feb 2021 • Arbaaz Khan, Vijay Kumar, Alejandro Ribeiro
We are able to demonstrate the scalability of our methods for a large number of robots by employing a graph neural network (GNN) to parameterize policies for the robots.
no code implementations • 11 Jun 2020 • Arbaaz Khan, Alejandro Ribeiro, Vijay Kumar, Anthony G. Francis
This paper investigates the feasibility of using Graph Neural Networks (GNNs) for classical motion planning problems.
no code implementations • 19 Feb 2019 • Clark Zhang, Arbaaz Khan, Santiago Paternain, Alejandro Ribeiro
In this paper, we investigate a method to regularize model learning techniques to provide better error characteristics for traditional control and planning algorithms.
no code implementations • 27 Sep 2018 • Arbaaz Khan, Clark Zhang, Vijay Kumar, Alejandro Ribeiro
A deep reinforcement learning solution is developed for a collaborative multiagent system.
no code implementations • 22 May 2018 • Arbaaz Khan, Clark Zhang, Daniel D. Lee, Vijay Kumar, Alejandro Ribeiro
When the number of agents increases, the dimensionality of the input and control spaces increase as well, and these methods do not scale well.
Distributed Optimization Multi-agent Reinforcement Learning +2
no code implementations • ICLR 2018 • Arbaaz Khan, Clark Zhang, Nikolay Atanasov, Konstantinos Karydis, Vijay Kumar, Daniel D. Lee
The third part uses a network controller that learns to store those specific instances of past information that are necessary for planning.
no code implementations • 23 May 2017 • Steven W. Chen, Nikolay Atanasov, Arbaaz Khan, Konstantinos Karydis, Daniel D. Lee, Vijay Kumar
This work is a first thorough study of memory structures for deep-neural-network-based robot navigation, and offers novel tools to train such networks from supervision and quantify their ability to generalize to unseen scenarios.