6 papers
Neural Low-Discrepancy Sequences
Michael Etienne Van Huffel, Nathan Kirk, Makram Chahine +2
Low-discrepancy points are designed to efficiently fill the space in a uniform manner. This uniformity is highly advantageous in many problems in science and engineering, including…
The Curious Case of In-Training Compression of State Space Models
Makram Chahine, Philipp Nazari, Daniela Rus +1
State Space Models (SSMs), developed to tackle long sequence modeling tasks efficiently, offer both parallelizable training and fast inference. At their core are recurrent dynamica…
Improving Efficiency of Sampling-based Motion Planning via Message-Passing Monte Carlo
Makram Chahine, T. Konstantin Rusch, Zach J. Patterson +1
Sampling-based motion planning methods, while effective in high-dimensional spaces, often suffer from inefficiencies due to irregular sampling distributions, leading to suboptimal…
Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring
Makram Chahine, William Yang, Alaa Maalouf +6
Wildlife field operations demand efficient parallel deployment methods to identify and interact with specific individuals, enabling simultaneous collective behavioral analysis, and…
Flex: End-to-End Text-Instructed Visual Navigation from Foundation Model Features
Makram Chahine, Alex Quach, Alaa Maalouf +2
End-to-end learning directly maps sensory inputs to actions, creating highly integrated and efficient policies for complex robotics tasks. However, such models often struggle to ge…
Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks
Alex Quach, Makram Chahine, Alexander Amini +2
Simulators are powerful tools for autonomous robot learning as they offer scalable data generation, flexible design, and optimization of trajectories. However, transferring behavio…