Hussein A. Abbass

About

Hussein A. Abbass is a prominent researcher whose work spans reinforcement learning, autonomous systems, human-swarm interaction, and computer vision for robotics. Best known for his pioneering contributions to hierarchical and multi-task deep reinforcement learning, Abbass developed innovative algorithms that enable robots to master compound tasks and multiple objectives simultaneously — his hierarchical deep reinforcement learning framework (2018) has garnered nearly 200 citations, while his multi-task approach reduced per-task network parameters by over 75%, representing a significant efficiency breakthrough. His research extends into monocular depth estimation, with a widely cited 2022 survey (160 citations) mapping the landscape of real-time solutions for autonomous driving and robotics, complemented by practical contributions such as MobileXNet, a computationally efficient depth estimation network. Abbass has also made substantial contributions to trusted autonomy, exploring how reliability and transparency shape human trust in swarm systems — a theme threading through his work on shepherding algorithms, swarm metaverse frameworks, and big data autonomy. His early investigation into evolutionary multiobjective optimization for embodied cognition further demonstrates the breadth of his intellectual curiosity. Collectively, his body of work reflects a sustained commitment to making autonomous systems smarter, more trustworthy, and practically deployable.

Research Focus

Key Achievements

Top Papers

1. 1
   Hierarchical Deep Reinforcement Learning for Continuous Action Control

   197 citations · 2018
2. 2
   Towards Real-Time Monocular Depth Estimation for Robotics: A Survey

   160 citations · 2022
3. 3
   Multi-Task Deep Reinforcement Learning for Continuous Action Control

   59 citations · 2017
4. 4
   The reliability and transparency bases of trust in human-swarm interaction: principles and implications

   37 citations · 2020
5. 5
   A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data

   35 citations · 2016
6. 6
   Multiobjectivity and Complexity in Embodied Cognition

   33 citations · 2005
7. 7
   MobileXNet: An Efficient Convolutional Neural Network for Monocular Depth Estimation

   27 citations · 2022
8. 8
   The Limits of Reactive Shepherding Approaches for Swarm Guidance

   26 citations · 2020
9. 9
   A Deep Hierarchical Reinforcement Learner for Aerial Shepherding of Ground Swarms

   25 citations · 2019
10. 10
    Swarm Metaverse for Multi-Level Autonomy Using Digital Twins

    16 citations · 2023

Key Collaborators

Contact & Links