53 citations · 232 across the 44 of their papers we have counts for
6 papers · 1 filter
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…
Joint Communication and Motion Planning for Cobots
Mehdi Dadvar, Keyvan Majd, Elena Oikonomou +2
The increasing deployment of robots in co-working scenarios with humans has revealed complex safety and efficiency challenges in the computation robot behavior. Movement among huma…
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…
Learn and Link: Learning Critical Regions for Efficient Planning
Daniel Molina, Kislay Kumar, Siddharth Srivastava
This paper presents a new approach to learning for motion planning (MP) where critical regions of an environment are learned from a given set of motion plans and used to improve pe…
An ROS-based Shared Communication Middleware for Plug & Play Modular Intelligent Design of Smart Systems
Tathagata Chakraborti, Siddharth Srivastava, Alessandro Pinto +1
Centralized architectures for systems such as smart offices and homes are rapidly becoming obsolete due to inherent inflexibility in their design and management. This is because su…