Tom Silver
Assistant Professor, Princeton University
Contact: tsilver@princeton.edu
I am an assistant professor at Princeton in the Electrical and Computer Engineering department and a core faculty member in robotics. I am also associated with the Center for Statistics and Machine Learning. I completed my PhD in May 2024 at MIT EECS, where I was advised by Leslie Kaelbling and Josh Tenenbaum. I was a postdoc with Tapo Bhattacharjee at Cornell in 2024-2025. I received my B.A. from Harvard with highest honors in computer science and mathematics in 2016.
I direct the Princeton Robot Planning and Learning (PRPL) lab. Our mission is to develop generalist robots that learn and plan to help people. Most of our work is at the intersection of automated planning and machine learning: learning to plan and planning to learn while making efficient use of limited data and time. We often use techniques from task and motion planning, program synthesis, foundation models, reinforcement learning, and neuro-symbolic ML. But we are driven by problems, not methods, and we are especially motivated to solve general problems in applications like robot caregiving where robots can empower people to remain independent.
news
| Jan 30, 2026 | Two new PRPL papers at ICLR 2026! SLAP (led by Isabel Liu) and ExoPredicator (led by Yichao Liang). |
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| Sep 30, 2025 | New work at CoRL 2025! SAVOR (oral, led by Zhanxin Wu), CLAMP (led by Pranav Thakkar), and PrioriTouch (led by Rishabh Madan). |
| Jun 24, 2025 | FEAST won the Best Paper Award at RSS 2025! |
| May 23, 2025 | New preprint: “Coloring Between the Lines: Personalization in the Null Space of Planning Constraints” (arxiv, website). |
| May 20, 2025 | Heading to ICRA 2025 to help organize the PhyRC Challenge! |
code
I am a big fan of open-source code and open science. I typically develop research projects out in the open, not in private repos. You can find the code for all past research projects led by me on my GitHub or linked from the respective papers. Also check out my lab's mono-repo, which has a number of shared utilities for planning, learning, and simulation.