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I was awarded the National Science Foundation Graduate Research Fellowship (NSF GRFP)!
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I was awarded the National Science Foundation Graduate Research Fellowship (NSF GRFP)!
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Our paper titled “POLICEd RL: Learning Closed-Loop Robot Control Policies with Provable Satisfaction of Hard Constraints” got accepted at the Robotics: Science and Systems (RSS) 2024!
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Our paper titled “Optimal Robotic Assembly Sequence Planning: A Sequential Decision-Making Approach” has been accepted to the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2024!
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Our paper titled “Learning to Provably Satisfy High Relative Degree Constraints for Black-Box Systems” got accepted at the 2024 Conference on Decision and Control (CDC)!
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Our paper titled “Leveraging Large Language Models for Effective and Explainable Multi-Agent Credit Assignment“ got accepted at the International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2025!
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Published in Texas ScholarWorks, 2021
Near-earth space is geopolitically and commercially contested, and in need of environmental protection. To achieve space safety, security, and sustainability, we are developing ASTRIAGraph, a framework that enables monitoring, assessment, and verification of space actor behavior in the context of legal and policy instruments.
Recommended citation: http://dx.doi.org/10.26153/tsw/11754
Published in AIAA Journal of Guidance, Control, and Dynamics, 2023
ONE of the challenges for flying quadrotors in cluttered envi-ronments is to optimize their trajectories subject to collision avoidance constraints in real time. Along such a trajectory, the position of the quadrotor must stay within a set of collision-free corridors. Each corridor is a bounded convex flight space.
Recommended citation: https://doi.org/10.2514/1.G007218
Published in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024
Proposed a new formulation of the robotic assembly sequencing problem as a Markov Decision Process. Then showed how a class of methods called Graph Exploration Assembly Planners (GEAPs) can be used to gather optimal assembly sequences from a graph. We then showcased a deep reinforcement learning extension for handling very complex structures, all while handling diverse constraints.
Recommended citation: [https://doi.org/10.1109/IROS58592.2024.10802475](https://doi.org/10.1109/IROS58592.2024.10802475)
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
Graduate Class, University of California, Mechanical Engineering, 2024
NVMD! BLOG POST INSTEAD