| Anytime integrated task and motion policies for stochastic environments N Shah, DK Vasudevan, K Kumar, P Kamojjhala, S Srivastava 2020 IEEE International Conference on Robotics and Automation (ICRA), 9285-9291, 2020 | 43 | 2020 |
| Using deep learning to bootstrap abstractions for hierarchical robot planning N Shah, S Srivastava Autonomous Agents and Multi-Agent Systems (AAMAS), 2022 | 29 | 2022 |
| Jedai: A system for skill-aligned explainable robot planning N Shah, P Verma, T Angle, S Srivastava Autonomous Agents and Multi-Agent Systems (AAMAS), 2021 | 16 | 2021 |
| From Reals to Logic and Back: Inventing Symbolic Vocabularies, Actions and Models for Planning from Raw Data N Shah, J Nagpal, P Verma, S Srivastava arXiv preprint arXiv:2402.11871, 2024 | 9 | 2024 |
| Learning Sampling Distributions for Efficient High‐Dimensional Motion Planning N Shah, A Srinet, S Srivastava ICAPS Workshop on Planning in Robotics (PlanRob), 2020 | 7* | 2020 |
| Hierarchical planning and learning for robots in stochastic settings using zero-shot option invention N Shah, S Srivastava Proceedings of the AAAI Conference on Artificial Intelligence 38 (9), 10358 …, 2024 | 5 | 2024 |
| Multi-task option learning and discovery for stochastic path planning N Shah, S Srivastava arXiv preprint arXiv:2210.00068, 2022 | 3 | 2022 |
| Perfect Observability is a Myth: Restraining Bolts in the Real World M Verma, N Shah, RK Nayyar, A Hanni | 3 | 2021 |
| Using Explainable AI and Hierarchical Planning for Outreach with Robots D Dobhal, J Nagpal, R Karia, P Verma, RK Nayyar, N Shah, S Srivastava arXiv preprint arXiv:2404.00808, 2024 | 1 | 2024 |
| Learning Neuro-Symbolic Abstractions for Robot Planning and Learning N Shah Proceedings of the AAAI Conference on Artificial Intelligence 38 (21), 23417 …, 2024 | 1 | 2024 |
| An Anytime Hierarchical Approach for Stochastic Task and Motion Planning N Shah, S Srivastava arXiv preprint arXiv:2108.12537, 2021 | 1 | 2021 |
| Anytime Stochastic Task and Motion Policies N Shah, S Srivastava arXiv preprint arXiv:2108.12537, 2021 | 1 | 2021 |
| Zero-Shot Option Invention for Robot Planning Under Uncertainty N Shah, S Srivastava CoRL 2023 Workshop on Learning Effective Abstractions for Planning (LEAP), 0 | 1 | |
| SkillWrapper: Skill Abstraction in the Era of Foundation Models SS Raman, Z Yang, B Hedegaard, S Tellex, D Paulius, N Shah 2nd CoRL Workshop on Learning Effective Abstractions for Planning, 2024 | | 2024 |
| Structured Exploration in Reinforcement Learning by Hypothesizing Linear Temporal Logic Formulas Y Wei, X Li, JX Liu, N Shah, B Quartey, G Konidaris, S Tellex, A Bagaria 2nd CoRL Workshop on Learning Effective Abstractions for Planning, 2024 | | 2024 |
| Autonomously Learning World-Model Representations For Efficient Robot Planning N Shah Arizona State University, 2024 | | 2024 |
| Reliable Neuro-Symbolic Abstractions for Planning and Learning. N Shah IJCAI, 7093-7094, 2023 | | 2023 |
| Learning to Create Abstraction Hierarchies for Motion Planning under Uncertainty N Shah, S Srivastava PRL Workshop Series {\textendash} Bridging the Gap Between AI Planning and …, 0 | | |
| Learning How to Create Generalizable Hierarchies for Robot Planning N Shah, S Srivastava NeurIPS 2023 Workshop on Generalization in Planning, 0 | | |
| Learning to Zero-Shot Invent Options for Robot Planning Under Uncertainty N Shah, S Srivastava | | |
Siddharth SrivastavaArizona State UniversityAdresse e-mail validée de asu.edu