Arbaaz Khan

Robotics and machine learning researcher

About

I am a second year PhD student in the Electrical and Systems Engineering Department at the University of Pennsylvania. I am advised by Professors Vijay Kumar and Alejandro Ribeiro at the GRASP lab. My research interests lie at the intersection of learning and decision making for robotics. These include improving upon existing policy optimization methodologies, neural network architectures and exploring new models for robust learning. Most recently I have been investigating reinforcement learning methods for teams of robots through parametrizations on graphs.

Before my PhD, I was pursuing a masters in Robotics also at the University of Pennsylvania where I was advised by Professors Daniel D. Lee and Vijay Kumar. During my masters, my research was focused on learning meaningful representations for robot navigation. I also spent some time working at the LAIR Lab at Carnegie Mellon University's Robotics Institute. At CMU, my research was focused on autonomous UAV flight through GPS denied cluttered outdoor environments such as forests.

News

Research




Graph Policy Gradients for Large Scale Unlabeled Motion Planning with Constraints
Submitted to International Conference on Robotics and Automation, (ICRA), 2020
A. Khan, V. Kumar, A. Ribeiro
Graph Policy Gradients for Large Scale Robot Control
3rd International Conference on Robot Learning (CoRL), 2019
A. Khan, E. Tolstaya, A. Ribeiro, V. Kumar
Learning Safe Unlabeled Multi-Robot Planning with Motion Constraints
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019
A. Khan, C. Zhang, S. Li, J. Wu, B. Schlotfeldt, S Y Tang, A. Ribeiro, O. Bastani, V. Kumar
Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients
A. Khan, C. Zhang, D.D. Lee, V. Kumar, A. Ribeiro
Learning Sample-Efficient Target Reaching for Mobile Robots
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018
A. Khan, V. Kumar, A. Ribeiro
Memory Augmented Control Networks
International Conference on Learning Representations (ICLR), 2018
A. Khan, C. Zhang, N. Atanasov, K. Karydis, V. Kumar, D.D. Lee
Multi Modal Pose Fusion for Monocular Flight with Unmanned Aerial Vehicles
2018 IEEE Aerospace Conference
A. Khan, M. Hebert
Learning Safe Recovery Trajectories with Deep Neural Networks for Unmanned Aerial Vehicles
2018 IEEE Aerospace Conference
A. Khan, M. Hebert
Neural Network Memory Architectures for Autonomous Robot Navigation
3rd Conference on Reinforcement Learning and Decision Making, 2017
S. Chen, N. Atanasov, A. Khan, K. Karydis, D.D. Lee, V. Kumar

Workshop Publications

End to End Memory Networks for Planning
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017
A. Khan, C. Zhang, N. Atanasov, K. Karydis, V. Kumar, Daniel D. Lee

End-to-End Navigation in Unknown Environments using Neural Networks
Workshop on Learning Perception, Control and Autonomous Flight: Safety, Memory and Efficiency at RSS 2017, Boston
A. Khan, C. Zhang, N. Atanasov, K. Karydis, V. Kumar, Daniel D. Lee

Robust Monocular Flight in Cluttered Outdoor Environments,
S. Daftry, S. Zeng, A. Khan, D. Dey, N. Melik-Barkhudarov, J.A Bagnell, M. Hebert

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