Soroush Nasiriany

Soroush Nasiriany

I am a CS PhD student at UT Austin, advised by Professor Yuke Zhu at the Robot Perception and Learning Lab. Previously, I recieved my undergraduate and master's degrees from UC Berkeley, where I was advised by Professor Sergey Levine.

I'm interested in building intelligent robot agents that can perform useful behaviors in diverse real world settings. Recently I've been working on harnessing diverse sources of data, namely internet data, robot simulation and real world robot data, to build a powerful foundation model for robotics.

	RT-Affordance: Affordances are Versatile Intermediate Representations for Robot Manipulation Soroush Nasiriany, Sean Kirmani, Tianli Ding, Laura Smith, Yuke Zhu, Danny Driess, Dorsa Sadigh, Ted Xiao arXiv preprint 2411.02704 Project Paper
	RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots Soroush Nasiriany, Abhiram Maddukuri, Lance Zhang, Adeet Parikh, Aaron Lo, Abhishek Joshi, Ajay Mandlekar, Yuke Zhu Robotics: Science and Systems (RSS), 2024 Project Paper Code
	DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset Alexander Khazatsky, Karl Pertsch, Suraj Nair, Ashwin Balakrishna, Sudeep Dasari, Siddharth Karamcheti, Soroush Nasiriany, ..., Yuke Zhu, Thomas Kollar, Sergey Levine, Chelsea Finn Robotics: Science and Systems (RSS), 2024 Project Paper Code
	PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learning Tian Gao, Soroush Nasiriany, Huihan Liu, Quantao Yang, Yuke Zhu IEEE Robotics and Automation Letters (RA-L), 2024 Project Paper
	PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs Soroush Nasiriany, Fei Xia, Wenhao Yu, Ted Xiao, Jacky Liang, Ishita Dasgupta, Annie Xie, Danny Driess, Ayzaan Wahid, Zhuo Xu, Quan Vuong, Tingnan Zhang, Tsang-Wei Edward Lee, Kuang-Huei Lee, Peng Xu, Sean Kirmani, Yuke Zhu, Andy Zeng, Karol Hausman, Nicolas Heess, Chelsea Finn, Sergey Levine, Brian Ichter* International Conference on Machine Learning (ICML), 2024 Project Paper Demo
	Open X-Embodiment: Robotic Learning Datasets and RT-X Models Open X-Embodiment Collaboration IEEE International Conference on Robotics and Automation (ICRA), 2024 Project Paper Code
	MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations Ajay Mandlekar, Soroush Nasiriany, Bowen Wen, Iretiayo Akinola, Yashraj Narang, Linxi Fan, Yuke Zhu, Dieter Fox Conference on Robot Learning (CoRL), 2023 Project Paper Code
	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 Project Paper Code
	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 Project Paper Code
	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 Project Paper Code
	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 Project Paper Code
	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 Project Paper Code
	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* Project Paper
	Planning with Goal-Conditioned Policies Soroush Nasiriany, Vitchyr H. Pong, Steven Lin, Sergey Levine Advances in Neural Information Processing Systems, 2019 Project Paper Code

Teaching

CS 391R: Robot Learning, UT Austin
CS 343: Artificial Intelligence, UT Austin
CS 189: Machine Learning, UC Berkeley (Course Notes)
CS 285: Deep Reinforcement Learning, UC Berkeley

Service

Organizer of UT Robot Learning Reading Group
Reviewer for CoRL, NeurIPS, ICRA, ICLR, ICML