• TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation
  D. Son, F. Shkurti, J. Lee, N. Shah, B. Kim, D. Fox
  arXiv preprint, 2026
  Paper
• Beyond Task and Motion Planning: Hierarchical Planning for General-Purpose Robot Skills
  B. Hedegaard, Z. Yang, Y. Wei, A. Jaafar, S. Tellex, G. Konidaris, N. Shah
  ICRA 2026
  Also presented at the ICRA 2025 Workshop on Language and Semantics of Task and Motion Planning.
  Paper
• Context-Sensitive Abstractions for Reinforcement Learning with Parameterized Actions
  R. K. Nayyar, N. Shah, S. Srivastava
  AAAI 2026
  Paper
• From the Real World to Logic and Back: Learning Symbolic World Models for Long Horizon Planning
  N. Shah, J. Nagpal, S. Srivastava
  CoRL 2025
  Also presented at the AAAI 2025 Workshop on Generalization in Planning (GenPlan) and the RSS 2025 Workshop on Robot Representations (RoboReps).
  Paper
• SkillWrapper: Autonomously Learning Interpretable Skill Abstractions with Foundation Models
  Z. Yang, S. S. Raman, B. Hedegaard, H. Fu, L. Zhao, S. Tellex, G. Konidaris, D. Paulius, N. Shah
  ICRA 2025 Workshop on Language and Semantics of Task and Motion Planning Best Paper Award
  Also presented at the CoRL 2024 Workshop on Learning Efficient Abstractions for Planning (LEAP).
  Paper
• Using Explainable AI and Hierarchical Planning for Outreach with Robots
  D. Dobhal, J. Nagpal, R. Karia, P. Verma, R. K. Nayyar, N. Shah, S. Srivastava
  EAAI / AAAI 2025
  Paper
• Structured Exploration in Reinforcement Learning by Hypothesizing Linear Temporal Logic Formulas
  Y. Wei, X. Li, J. Liu, N. Shah, B. Quartey, G. Konidaris, S. Tellex, A. Bagaria
  CoRL 2024 Workshop on Learning Efficient Abstractions for Planning (LEAP)
  Paper
• Hierarchical Planning and Learning for Robots in Stochastic Settings Using Zero-Shot Option Invention
  N. Shah, S. Srivastava
  AAAI 2024
  Paper
• Learning to Create Abstraction Hierarchies for Motion Planning Under Uncertainty
  N. Shah, S. Srivastava
  IJCAI 2023 Workshop on Bridging the Gap: Planning and Reinforcement Learning (PRL)
  Paper
• Learning Neuro-Symbolic Abstractions for Motion Planning Under Uncertainty
  N. Shah, S. Srivastava
  IJCAI 2023 Workshop on Neuro-Symbolic Agents (NSA)
  Paper
• An Anytime Hierarchical Approach for Stochastic Task and Motion Planning
  N. Shah, S. Srivastava
  arXiv preprint, 2022
  Paper
• JEDAI: A System for Skill-Aligned Explainable Robot Planning
  N. Shah*, P. Verma*, T. Angle, S. Srivastava
  AAMAS 2022 (Demonstration Track) Best Demo Award
  • Equal contribution.
  PaperDemo
• Using Deep Learning to Bootstrap Abstractions for Robot Planning
  N. Shah, S. Srivastava
  AAMAS 2022
  Paper
• Learning and Using Abstractions for Robot Planning
  N. Shah, A. Srinet, S. Srivastava
  ICAPS 2021 Workshop on Planning and Robotics (PlanRob)
  PaperTalk
• Anytime Integrated Task and Motion Policies for Stochastic Environments
  N. Shah, D. K. Vasudevan, K. Kumar, P. Kamojjhala, S. Srivastava
  ICRA 2020
  PaperCodeVideos
• Anytime Integrated Task and Motion Policies for Stochastic Environments
  N. Shah, S. Srivastava
  ICAPS 2019 Workshop on Planning and Robotics (PlanRob)
  Paper

Patents

• Learning Abstractions for Multi-Robot Path Planning in Unstructured Environments
  N. Shah, G. Fainekos, B. Hoxha, H. Okamoto, D. V. Prokhorov
  U.S. patent application 18/614,355 (under review), 2025
  Patent
• Robot Programming for Non-Programmers
  R. Karia, D. Dobhal, J. Nagpal, P. Verma, N. Shah, S. Srivastava
  U.S. patent application (under review), 2025