5 citations · 14 across the 8 of their papers we have counts for
9 papers
Context-Sensitive Abstractions for Reinforcement Learning with Parameterized Actions
Rashmeet Kaur Nayyar, Naman Shah, Siddharth Srivastava
Real-world sequential decision-making often involves parameterized action spaces that require both, decisions regarding discrete actions and decisions about continuous action param…
Using Explainable AI and Hierarchical Planning for Outreach with Robots
Rushang Karia, Jayesh Nagpal, Daksh Dobhal +4
Understanding how robots plan and execute tasks is crucial in today's world, where they are becoming more prevalent in our daily lives. However, teaching non-experts, such as K-12…
From Real World to Logic and Back: Learning Generalizable Relational Concepts For Long Horizon Robot Planning
Naman Shah, Jayesh Nagpal, Siddharth Srivastava
Robots still lag behind humans in their ability to generalize from limited experience, particularly when transferring learned behaviors to long-horizon tasks in unseen environments…
Multi-Task Option Learning and Discovery for Stochastic Path Planning
Naman Shah, Siddharth Srivastava
This paper addresses the problem of reliably and efficiently solving broad classes of long-horizon stochastic path planning problems. Starting with a vanilla RL formulation with a…
Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning
Naman Shah, Siddharth Srivastava
This paper addresses the problem of learning abstractions that boost robot planning performance while providing strong guarantees of reliability. Although state-of-the-art hierarch…
JEDAI: A System for Skill-Aligned Explainable Robot Planning
Naman Shah, Pulkit Verma, Trevor Angle +1
This paper presents JEDAI, an AI system designed for outreach and educational efforts aimed at non-AI experts. JEDAI features a novel synthesis of research ideas from integrated ta…
An Anytime Hierarchical Approach for Stochastic Task and Motion Planning
Naman Shah, Siddharth Srivastava
In order to solve complex, long-horizon tasks, intelligent robots need to carry out high-level, abstract planning and reasoning in conjunction with motion planning. However, abstra…
Learning and Using Abstractions for Robot Planning
Naman Shah, Abhyudaya Srinet, Siddharth Srivastava
Robot motion planning involves computing a sequence of valid robot configurations that take the robot from its initial state to a goal state. Solving a motion planning problem opti…
Anytime Integrated Task and Motion Policies for Stochastic Environments
Naman Shah, Deepak Kala Vasudevan, Kislay Kumar +2
In order to solve complex, long-horizon tasks, intelligent robots need to carry out high-level, abstract planning and reasoning in conjunction with motion planning. However, abstra…