I am a James McDonnell Postdoctoral Fellow at GRASP Labs in Computer and Information Science at University of Pennsylvania, Philadelphia, PA, USA. I finished my PhD at the School of Computer Science of McGill University. My area of research is an intersection between mobile robotics and machine learning with a focus on designing algorithms for autonomous vehicles to sample, understand, and map challenging environments. My main interests are in robotic active sampling, reinforcement learning, robotic sensor networks, multi-robot coordination, machine learning and their applications for building autonomous agents that can adapt to different surroundings they are placed in.
In this project, we present an adaptive sampling technique that generates paths to efficiently measure and then mathematically model a scalar field by performing non-uniform measurements in a given region of interest. We compute a sampling path that minimizes the expected time to accurately model the phenomenon of interest by visiting high information regions using non-myopic path generation based on reinforcement learning.
Physical sampling of water for off-site analysis is necessary for many applications like monitoring the quality of drinking water in reservoirs, understanding marine ecosystems, and measuring contamination levels in fresh-water systems. We present a multi-robot, data-driven, watersampling strategy, where autonomous surface vehicles plan and execute water sampling using the chlorophyll density as a cue for plankton-rich water samples
An ideal map of the scalar field requires complete coverage of the region, but can be approximated by a good sparse coverage strategy along with an efficient interpolation technique. We propose to optimize the trade off between the environmental field mapping and the costs (energy consumed, time spent, and distance traveled) associated with sensing. We present an anytime algorithm for sampling the environment adaptively by following a multi-scale path to produce a variable resolution map of the spatial field.