| 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 | 30 | 2020 |
| Using deep learning to bootstrap abstractions for hierarchical robot planning N Shah, S Srivastava Autonomous Agents and Multi-Agent Systems (AAMAS), 2022 | 22 | 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 | 12 | 2021 |
| Learning Sampling Distributions for Efficient High‐Dimensional Motion Planning N Shah, A Srinet, S Srivastava ICAPS Workshop on Planning in Robotics (PlanRob), 2020 | 4* | 2020 |
| Perfect Observability is a Myth: Restraining Bolts in the Real World M Verma, N Shah, RK Nayyar, A Hanni | 3 | 2021 |
| Multi-Task Option Learning and Discovery for Stochastic Path Planning N Shah, S Srivastava arXiv preprint arXiv:2210.00068, 2022 | 2 | 2022 |
| 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 | 1 | 2024 |
| Hierarchical planning and learning for robots in stochastic settings using zero-shot option invention N Shah, S Srivastava Proc. AAAI, 2024 | 1 | 2024 |
| Anytime Stochastic Task and Motion Policies N Shah, S Srivastava arXiv preprint arXiv:2108.12537, 2021 | 1 | 2021 |
| Learning Hierarchical Abstractions for Efficient Taskable Robots–Dissertation Abstract N Shah 32nd International Conference on Automated Planning and Scheduling, 32, 0 | | |
Siddharth SrivastavaArizona State Universityasu.edu의 이메일 확인됨
