The Personal Autonomous Robotics Lab (PeARL)
Director: Scott Niekum
The goal of our research is to enable personal robots to be deployed in the home and workplace with minimal intervention by robotics experts. In settings such as these, robots do not operate in isolation, but have continual interactions with people and objects in the world. With this in mind, we focus on developing algorithms to solve problems that robot learners encounter in real-world interactive settings. Thus, our work draws roughly equally from both machine learning and robotics, including topics such as learning from demonstration, manipulation, human-robot interaction, interactive perception, and reinforcement learning.
Daniel Brown Ph.D. Student dsbrown [at] cs utexas edu
Caleb Chuck Ph.D. Student caleb_chuck [at] yahoo com
Thomas Crosley Ph.D. Student crosleythomas [at] gmail com
Yuchen Cui Ph.D. Student yuchencui [at] utexas edu
Wonjoon Goo Ph.D. Student wonjoon [at] cs utexas edu
Reymundo A. Gutierrez Ph.D. Student
Ajinkya Jain Ph.D. Student
Scott Niekum Faculty sniekum [at] cs utexas edu
Akanksha Saran Ph.D. Student asaran [at] cs utexas edu
Using Natural Language to Aid Task Specification in Sequential Decision Making Problems 2021
Prasoon Goyal, Ph.D. Proposal.
PixL2R: Guiding Reinforcement Learning using Natural Language by Mapping Pixels to Rewards 2020
Prasoon Goyal, Scott Niekum, Raymond J. Mooney, In 4th Conference on Robot Learning (CoRL), November 2020. Also presented on the 1st Language in Reinforcement Learning (LaReL) Workshop at ICML, July 2020 (Best Paper Award), the 6th Deep Rein...
Using Natural Language for Reward Shaping in Reinforcement Learning 2019
Prasoon Goyal, Scott Niekum, Raymond J. Mooney, In Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China, August 2019.