5 citations · 7 across the 2 of their papers we have counts for
4 papers
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…
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…