Scholar
Tom Silver
Google Scholar ID: CMcsygMAAAAJ
Assistant Professor at Princeton
Planning
Learning
Robotics
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Citations & Impact
All-time
Citations
2,731
H-index
21
i10-index
28
Publications
20
Co-authors
0
Contact
Email
tsilver@princeton.edu
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Publications
16 items
A Human-in-the-Loop Confidence-Aware Failure Recovery Framework for Modular Robot Policies
2026
Cited
0
Unifying Deep Predicate Invention with Pre-trained Foundation Models
2025
Cited
0
SLAP: Shortcut Learning for Abstract Planning
2025
Cited
0
ExoPredicator: Learning Abstract Models of Dynamic Worlds for Robot Planning
2025
Cited
0
PrioriTouch: Adapting to User Contact Preferences for Whole-Arm Physical Human-Robot Interaction
2025
Cited
0
FEAST: A Flexible Mealtime-Assistance System Towards In-the-Wild Personalization
2025
Cited
1
SAVOR: Skill Affordance Learning from Visuo-Haptic Perception for Robot-Assisted Bite Acquisition
2025
Cited
0
CLAMP: Crowdsourcing a LArge-scale in-the-wild haptic dataset with an open-source device for Multimodal robot Perception
2025
Cited
0
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Resume (English only)
Academic Achievements
Paper 'FEAST: A Flexible Mealtime Assistance System Towards In-the-Wild Personalization' won Best Paper Award at RSS 2025
Three papers accepted at CoRL 2025: SAVOR (oral, led by Zhanxin Wu), CLAMP (led by Pranav Thakkar), and PrioriTouch (led by Rishabh Madan)
Two papers at RSS 2025: 'Bilevel Learning for Bilevel Planning' (led by Bowen Li) and 'FEAST' (led by Rajat Kumar Jenamani)
Preprint released in May 2025: 'Coloring Between the Lines: Personalization in the Null Space of Planning Constraints'
Strong advocate for open-source code and open science; all research projects led by him are developed publicly
Background
Assistant Professor in the Department of Electrical and Computer Engineering at Princeton University and core faculty member in robotics
Affiliated with the Center for Statistics and Machine Learning
Directs the Princeton Robot Planning and Learning (PRPL) lab, aiming to develop generalist robots that learn and plan to help people
Research lies at the intersection of automated planning and machine learning, emphasizing efficient use of limited data and time
Employs techniques such as task and motion planning, program synthesis, foundation models, reinforcement learning, and neuro-symbolic ML
Problem-driven, with a strong focus on assistive applications like robot caregiving to empower human independence
Co-authors
0 total
Co-authors: 0 (list not available)
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