Senthil Hariharan Arul
Ph.D. Candidate at University of Maryland College Park
Robotics | Motion Planning | Planning under Uncertainty | Reinforcement Learning
I am a Ph.D. candidate in Electrical and Computer Engineering at the University of Maryland, College Park, specializing in multi-agent navigation within complex environments under the guidance of Prof. Dinesh Manocha. My research integrates robotics, motion planning, and reinforcement learning to advance AI technology. Specifically, I specialize in cooperative navigation and motion planning under uncertainty, exploring innovative solutions for real-world applications. In addition, I completed two internships at Amazon Lab126, specifically with the Consumer Robotics group.
I hold a bachelor’s degree in Instrumentation and Control Engineering from the National Institute of Technology, Tiruchirappalli. I interned for a summer at McMaster University, Canada, with Prof. Gray Bone, where I was
involved in the development of an autonomous collaborative robotic arm. I am
proficient in C++, Python, and TensorFlow, with a strong publication record in
top-tier robotics and AI conferences.
news
| Feb 10, 2025 | This spring, I am working as a Research Intern at Honda Research Institute (HRI), San Jose, focusing on Behavior Modeling and Interactive Planning for Autonomous Vehicles. |
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| Nov 06, 2024 | Delivered a talk at Amazon Lab126’s Consumer Robotics Student Summit titled “Navigating the Everyday: Improving Robot Mobility in Household Scenarios.” |
| Oct 17, 2024 | Two papers accepted at IROS 2024: “VLPG-Nav: Object Navigation Using Visual Language Pose Graph and Object Localization Probability Maps” and “When, What, and with Whom to Communicate: Enhancing RL-based Multi-Robot Navigation through Selective Communication.” |
| May 31, 2023 | Spending the summer as an Applied scientist intern at Amazon Lab126, Sunnyvale working on Reliable Object Goal Navigation in household scenes. |
| Apr 15, 2023 | Delivered a talk at the FLAIR Talk Series, University of Oxford, on “Decentralized Multi-Agent Navigation in Complex Scenarios.” |