activity
20182023
most citedTemporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks

29 citations · 35 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2023

NeuroCUT: A Neural Approach for Robust Graph Partitioning

Rishi Shah, Krishnanshu Jain, Sahil Manchanda +2

Graph partitioning aims to divide a graph into disjoint subsets while optimizing a specific partitioning objective. The majority of formulations related to graph partitioning exhib…

cs.AI2020★ 29 cited

Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks

Yuqian Jiang, Sudarshanan Bharadwaj, Bo Wu +3

In continuing tasks, average-reward reinforcement learning may be a more appropriate problem formulation than the more common discounted reward formulation. As usual, learning an o…

cs.LG2020

Deep R-Learning for Continual Area Sweeping

Rishi Shah, Yuqian Jiang, Justin Hart +1

Coverage path planning is a well-studied problem in robotics in which a robot must plan a path that passes through every point in a given area repeatedly, usually with a uniform fr…

cs.RO2019★ 4 cited

Solving Service Robot Tasks: UT Austin Villa@Home 2019 Team Report

Rishi Shah, Yuqian Jiang, Haresh Karnan +8

RoboCup@Home is an international robotics competition based on domestic tasks requiring autonomous capabilities pertaining to a large variety of AI technologies. Research challenge…

cs.RO2018★ 2 cited

Interaction and Autonomy in RoboCup@Home and Building-Wide Intelligence

Justin Hart, Harel Yedidsion, Yuqian Jiang +8

Efforts are underway at UT Austin to build autonomous robot systems that address the challenges of long-term deployments in office environments and of the more prescribed domestic…