Nitish Dashora

I am an incoming PhD student at MIT CSAIL studying continual learning and robotic AI. I am advised by Pulkit Agrawal and am thankful to be supported by the NSF GRFP. Currently, I am a post-bacc at UC Berkeley where I am a researcher at Berkeley Artificial Intelligence Research under Sergey Levine. I previously worked with Bruno Olshausen from the Redwood Center for Theoretical Neuroscience. Lastly, I was a member of Machine Learning at Berkeley where I led research projects, made free course content, and mentored incoming students. I recieved my B.S. in Electrical Engineering and Computer Science (EECS) from UC Berkeley with Honors.

Summer 2023: I was a research intern at the Center for Human-Compatible AI working under Prof. Stuart Russell to study human-inspired exploration and skill discovery paradigms.
Summer 2022: I was a software development engineer intern at Amazon working with Amazon Web Services to create optimized Spark/Hadoop algorithms that use multiple clusters from load balancing across sharded data.
Summer 2021: I was a research fellow at NASA JPL where I worked with team CoStar, a joint MIT-Caltech-NASA organization for robotics research. I specifically worked with skill development for robotic planning through deep learning techniques.
Fall 2021: I was a Schmidt Futures AI Fellow for Educational AI Technology and entrepreneurship.

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I am most interested in the theory of intelligence and how to create general learning systems that encompass this definition. The age-old question of AGI has so strongly captivated me that I am certain I would like to dedicate my life’s career to exploring it. I think that physical embodiment will play a significant role in the creation of AGI, thus I find that robotics and robot learning is an excellent testbed for AI research. Currently, I work in computer vision and reinforcement learning to create rich perceptual learning systems and decision-making agents. My current research under Prof. Sergey Levine at BAIR involves zero-shot reward shaping for online RL and subgoal reaching. I also have interests in representation learning which I pursued with Prof. Bruno Olshausen by exploring object-part relationships and deformation through Restricted Boltzmann Machines.

Deep Hierarchical Laplacian Skill Discovery
TBD Authors
First Author

We present a compositional skill discovery feedback loop for fast online exploration and planning.

ViNT: A Foundation Model for Visual Navigation
Dhruv Shah*, Ajay Sridhar*, Nitish Dashora*, Kyle Stachowicz, Kevin Black, Noriaki Hirose, Sergey Levine
CoRL 2023 (Oral), BayLearn 2023 (Oral), Robot Learning @ NeurIPS 2023. Equal First Author.
project page / arXiv

We present a multi-robot visual navigation foundation model for effective fine-tuning and novel task adaptation.

Imitative Models for Passenger-Scale Autonomous Off-Road Driving
Nitish Dashora*, Sunggoo Jung*, Valentin Ibars, Osher Lerner, Calvin Jung, Dhruv Shah, Nicholas Rhinehart, Ali Agha-Mohammadi
IPPC @ IROS 2023, LRSA @ IROS 2023. First Author.

DAgger-like imitative learning with model predictive path integral control works as an end-to-end navigation system.

Hybrid Imitative Planning with Geometric and Predictive Costs in Off-road Environments
Nitish Dashora*, Daniel Shin*, Dhruv Shah, Henry Leopold,
David Fan, Ali Agha-Mohammadi, Nicholas Rhinehart, Sergey Levine
ICRA 2022. First Author.
project page / arXiv / video

Combining geometric costmaps and learned models improves long-range navigation in offroad environments.

Poster Object-Part Learning with Local Capsule Hierarchies
Nitish Dashora*, Silas Alberti*, Domas Buracas*, Bruno Olshausen,
Berkeley EECS and Research Symposium 2021. First Author.

Hierarchical Sparse Codes with learned steering can handle MNIST deformations.

CS 188 Logo Undergraduate Student Instructor (uGSI), CS188 - Intro to AI (Spring 2023)
CS 188 Logo Undergraduate Student Instructor (uGSI), CS188 - Intro to AI (Fall 2022)
CS 188 Logo Undergraduate Student Instructor (uGSI), CS188 - Intro to AI (Spring 2022)
Berkeley EECS Logo Reader, EECS 16B - Information Devices (Fall 2021)
Berkeley EECS Logo Academic Intern, CS 70 - Discrete Math and Probability (Spring 2021)