Multi-Objective Planning Using Context-Based Preferences at Oregon State University
Corvallis, Oregon, United States
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Summary
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Pulkit Rustagi is a Robotics Ph.D. candidate at Oregon State University with 10 years of experience designing scalable multi-agent and multi-objective planning systems for real robots. He developed the Contextual Lexicographic MDP framework and algorithms that fuse context-specific policies into global, cycle-free controllers validated on Turtlebots, and also created RECON to mitigate negative side effects in systems scaling to ~1000 agents. His work blends theory (contraction-based L1 adaptive control, credit assignment for blame) with hands-on firmware and ROS/Gazebo implementation, demonstrated across UAV swarms and mobile robots. Pulkit has driven projects from high-accuracy UWB localization to satellite attitude control, showing a consistent ability to translate advanced control and planning research into field-tested systems. An understated strength is his knack for bridging simulation and hardware, ensuring algorithms remain robust when deployed in the real world.
10 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD, Robotics, Doctor of Philosophy - PhD, Robotics at Oregon State University
University of Illinois Urbana-Champaign
Bachelor of Technology - BTech, Bachelor of Technology - BTech at Indian Institute of Technology, Kharagpur
Contributions:16 pushes, 2 branches in 1 year 1 month
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