Fara-7B: An Efficient Agentic Model for Computer Use
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Aravind Rajeswaran
Aravind Rajeswaran
Aravind Rajeswaran
Research Scientist at Microsoft AI Frontiers
Visiting PostDoc / Collaborator,
Berkeley AI Research Lab (BAIR)
I'm interested in building generalist AI agents that can operate in the open world. Towards this goal, my research brings together elements of reinforcement learning, representation learning, and world models. Prior to my role at Microsoft, I spent 4 years at Meta (FAIR) working on Embodied AI with Jitendra Malik, Dhruv Batra, and Abhinav Gupta. In addition to my industry roles, I've also collaborated closely with the academic labs of Prof. Pieter Abbeel at UC Berkeley and Prof. Abhinav Gupta at CMU. I recieved my PhD in Computer Science from the University of Washington working with Profs. Sham Kakade and Emo Todorov. During this time, I also worked closely with Sergey Levine and Chelsea Finn, and spent time as a student researcher at Google Brain and OpenAI. Before that, I recieved my bachelors and the best undergraduate thesis award from IIT Madras working with Balaraman Ravindran.
Representative Projects
AI Agents - Perception & Action
OpenEQA: Embodied Question Answering in the Era of Foundation Models
Cortex Team @ FAIR (Role: PI and Project Lead)
Computer Vision and Pattern Recognition (CVPR) 2024 | Project Website
Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D
Cortex Team @ FAIR (Role: PI and Tech Lead)
International Conference on Machine Learning (ICML) 2025 | Project Website
VC-1: Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?
Cortex Team @ FAIR (Role: PI and Tech Lead)
Neural Information Processing Systems (NeurIPS) 2023 | Project Website
Reinforcement Learning
Decision Transformer: Reinforcement Learning via Sequence Modeling
Lili Chen*, Kevin Lu*, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch
Neural Information Processing Systems (NeurIPS) 2021 | Project Website
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, John Schulman, Emanuel Todorov, Sergey Levine
Robotics: Science and Systems (RSS) 2018 | Project Website
Masked Trajectory Models for Prediction, Representation, and Control
Philipp Wu, Arjun Majumdar, Kevin Stone, Yixin Lin, Igor Mordatch, Pieter Abbeel, Aravind Rajeswaran
International Conference on Machine Learning (ICML) 2023 | Project Website
MOReL: Model-Based Offline Reinforcement Learning
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims
eural Information Processing Systems (NeurIPS) 2020 | Project Website
Mentoring
I enjoy collaborating with a diverse set of students and researchers. I have had the pleasure of mentoring some highly motivated students at both the undergraduate and PhD levels.
Interns
- Philipp Wu (PhD at Berkeley ==> startup)
- Arjun Majumdar (PhD at GATech ==> RS at Meta)
- Anurag Ajay (PhD at MIT ==> RS at DeepMind)
- Mandi Zhao (PhD at Stanford)
- Nicklas Hansen (PhD at UCSD)
- Shikhar Bahl (PhD at CMU ==> RS at Skild.AI)
- Allan Zhou (PhD at Stanford ==> RE at DeepMind)
- Suraj Nair (PhD at Stanford ==> RS at PI)
- Liyiming Ke (PhD at UW Seattle ==> RS at PI)
- Andi Peng (PhD at MIT ==> RS at Anthropic)
- Yuchen Cui (PhD at UT Austin ==> Prof at UCLA)
University Students and AI Residents
- Aryan Jain (UC Berkeley, BS ==> RE at Tesla Optimus)
- Sergio Arnaud (AI Resident, FAIR ==> RE at Meta)
- Rafael Rafailov (Stanford, MS ==> Stanford, PhD)
- Karmesh Yadav (CMU MS ==> Georgia Tech, PhD)
- Kevin Lu (UC Berkeley, BS ==> FAIR AI resident)
- Ben Evans (UW, BS/MS ==> PhD at NYU)
- Sarvjeet Ghotra (IITM Intern ==> PhD at MILA)
- Colin Summers (UW, BS/MS ==> UW PhD)
- Catherine Cang (UC Berkeley, BS ==> SWE at Plaid)
- Divye P. Jain (UW, BS/MS ==> Google MTV)
Teaching
CSE599G: Deep Reinforcement Learning (Instructor)
I designed and co-taught a course on deep reinforcement learning at UW in Spring 2018. The course presents a rigorous mathematical treatment of various RL algorithms along with illustrative applications in robotics. Deep RL courses at UW, MIT, and CMU have borrowed and built upon the material I developed for this course.
CSE547: Machine Learning for Big Data (Teaching Assistant)
This is an advanced graduate level course on machine learning with emphasis on machine learning at scale and distributed algorithms. Topics covered include hashing, sketching, streaming, large-scale distributed optimization, federated learning, and contextual bandits. I was the lead TA for this class.