no code implementations • 21 Aug 2020 • Mohammed Hossny, Julie Iskander
In this work we address the error propagation problem by introducing an iterative training procedure for deep reinforcement learning which allows the agent to learn a finite set of actions and how to coordinate between them in order to achieve a stable standing posture.
no code implementations • 12 Aug 2020 • Julie Iskander, Mohammed Hossny
Reinforcement learning has been applied to human movement through physiologically-based biomechanical models to add insights into the neural control of these movements; it is also useful in the design of prosthetics and robotics.
no code implementations • 8 Jun 2020 • Mohammed Hossny, Khaled Saleh, Mohammed Attia, Ahmed Abobakr, Julie Iskander
In this paper, we present a novel method to simulate LiDAR point cloud with faster rendering time of 1 sec per frame.
no code implementations • 4 Jun 2020 • Mohammed Hossny, Julie Iskander, Mohammed Attia, Khaled Saleh
In this paper, we propose enhancing actor-critic reinforcement learning agents by parameterising the final actor layer which produces the actions in order to accommodate the behaviour discrepancy of different actuators, under different load conditions during interaction with the environment.
no code implementations • 22 May 2019 • Khaled Saleh, Ahmed Abobakr, Mohammed Attia, Julie Iskander, Darius Nahavandi, Mohammed Hossny
We have evaluated the performance of our proposed framework on the task of vehicle detection from a bird's eye view (BEV) point cloud images coming from real 3D LiDAR sensors.
Ranked #2 on Unsupervised Domain Adaptation on PreSIL to KITTI