Soroush Nasiriany

I am a CS PhD student at UT Austin, advised by Professor Yuke Zhu at the Robot Perception and Learning Lab. I'm working towards developing versatile robot agents that can be deployed in the real world, using tools from machine learning, cognitive science, and computer vision.

Previously, I recieved my undergraduate and master's degrees from UC Berkeley, where I was advised by Professor Sergey Levine. During my time at Berkeley, I helped develop the curriculum for CS 189, the machine learning course.


Robot Learning on the Job: Human-in-the-Loop Manipulation and Learning During Deployment
Huihan Liu, Soroush Nasiriany, Lance Zhang, Zhiyao Bao, Yuke Zhu
Robotics: Science and Systems (RSS), 2023
Best Paper Award Finalist
Learning and Retrieval from Prior Data for Skill-based Imitation Learning
Soroush Nasiriany, Tian Gao, Ajay Mandlekar, Yuke Zhu
Conference on Robot Learning (CoRL), 2022
Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks
Soroush Nasiriany, Huihan Liu, Yuke Zhu
IEEE International Conference on Robotics and Automation (ICRA), 2022
Outstanding Learning Paper
What Matters in Learning from Offline Human Demonstrations for Robot Manipulation
Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li Fei-Fei, Silvio Savarese, Yuke Zhu, Roberto Martín-Martín
Conference on Robot Learning (CoRL), 2021
Oral Presentation
robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
Yuke Zhu, Josiah Wong, Ajay Mandlekar, Roberto Mart ́ın-Mart ́ın, Abhishek Joshi, Soroush Nasiriany, Yifeng Zhu
Technical report, 2020
DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies
Soroush Nasiriany*, Vitchyr H. Pong*, Ashvin Nair*, Alexander Khazatsky, Glen Berseth, Sergey Levine
IEEE International Conference on Robotics and Automation (ICRA), 2021
Planning with Goal-Conditioned Policies
Soroush Nasiriany*, Vitchyr H. Pong*, Steven Lin, Sergey Levine
Advances in Neural Information Processing Systems, 2019

Teaching and Service