13 citations · 35 across the 13 of their papers we have counts for
13 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…
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…
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…
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…
Local Non-Cooperative Games with Principled Player Selection for Scalable Motion Planning
Makram Chahine, Roya Firoozi, Wei Xiao +2
Game-theoretic motion planners are a powerful tool for the control of interactive multi-agent robot systems. Indeed, contrary to predict-then-plan paradigms, game-theoretic planner…
Follow Anything: Open-set detection, tracking, and following in real-time
Alaa Maalouf, Ninad Jadhav, Krishna Murthy Jatavallabhula +5
Tracking and following objects of interest is critical to several robotics use cases, ranging from industrial automation to logistics and warehousing, to healthcare and security. I…
Learning Stability Attention in Vision-based End-to-end Driving Policies
Tsun-Hsuan Wang, Wei Xiao, Makram Chahine +3
Modern end-to-end learning systems can learn to explicitly infer control from perception. However, it is difficult to guarantee stability and robustness for these systems since the…
Towards Cooperative Flight Control Using Visual-Attention
Lianhao Yin, Makram Chahine, Tsun-Hsuan Wang +5
The cooperation of a human pilot with an autonomous agent during flight control realizes parallel autonomy. We propose an air-guardian system that facilitates cooperation between a…
Intention Communication and Hypothesis Likelihood in Game-Theoretic Motion Planning
Makram Chahine, Roya Firoozi, Wei Xiao +2
Game-theoretic motion planners are a potent solution for controlling systems of multiple highly interactive robots. Most existing game-theoretic planners unrealistically assume a p…
Liquid Structural State-Space Models
Ramin Hasani, Mathias Lechner, Tsun-Hsuan Wang +3
A proper parametrization of state transition matrices of linear state-space models (SSMs) followed by standard nonlinearities enables them to efficiently learn representations from…
Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving
Wei Xiao, Tsun-Hsuan Wang, Makram Chahine +3
Guaranteeing safety of perception-based learning systems is challenging due to the absence of ground-truth state information unlike in state-aware control scenarios. In this paper,…