Nitish Dashora

Nitish Dashora

he/him/his

AI Researcher

Posts by Collection

portfolio

publications

Hybrid Imitative Planning with Geometric and Predictive Costs in Off-road Environments

Published in International Conference on Robotics and Automation, 2022

Combining geometric costmaps and learned models improves long-range navigation in offroad environments.
Also appeared as: Workshop Paper at NeurIPS, YouTube Video, and Project Site

Recommended citation: Nitish Dashora*, Daniel Shin*, Dhruv Shah, Henry Leopold, David Fan, Ali Agha-Mohammadi, Nicholas Rhinehart, Sergey Levine. (2022). "Hybrid Imitative Planning with Geometric and Predictive Costs in Off-road Environments." In the proceedings of 2022 International Conference on Robotics and Automation (ICRA).
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Imitative Models for Passenger-Scale Autonomous Off-Road Driving

Published in IPPC, LRSA @ International Conference on Intelligent Robots and Systems, 2023

DAgger-like imitative learning with model predictive path integral control works as an end-to-end navigation system.
Also appeared as: YouTube Video

Recommended citation: Nitish Dashora, Sunggoo Jung, Dhruv Shah, Valentin Ibars, Osher Lerner, Chanyoung Jung, Rohan Thakker, Nicholas Rhinehart, Ali-akbar Agha-mohammadi. (2022). "Imitative Models for Passenger-Scale Autonomous Off-Road Driving." Presented at 2023 International Conference on Intelligent Robots and Systems (IROS).
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ViNT: A Foundation Model for Visual Navigation

Published in Conference on Robot Learning (Oral), 2023

We present a multi-robot visual navigation foundation model for effective fine-tuning and novel task adaptation.
Also appeared as: Oral Presentation at BayLearn, Live Demo at CoRL, Workshop Paper at NeurIPS, YouTube Video, and Project Site

Recommended citation: Dhruv Shah*, Ajay Sridhar*, Nitish Dashora*, Kyle Stachowicz, Kevin Black, Noriaki Hirose, Sergey Levine. (2023). "ViNT: A Large-Scale, Multi-Task Visual Navigation Backbone with Cross-Robot Generalization." In the proceedings of 2023 Conference on Robot Learning (CoRL).
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talks

teaching

Academic Intern (CS 70)

Undergraduate course, UC Berkeley, EECS, 2021

I worked on staff for CS 70 - Introduction Discrete Math and Probability Theory with Satish Rao for 1 semester contributing as a discussion assistant. This course covers logic, infinity, and induction; applications include undecidability and stable marriage problem. Modular arithmetic and GCDs; applications include primality testing and cryptography. Polynomials; examples include error correcting codes and interpolation. Probability including sample spaces, independence, random variables, law of large numbers; examples include load balancing, existence arguments, Bayesian inference. Current course offering here.

Course Reader (EECS 16B)

Undergraduate course, UC Berkeley, EECS, 2021

I graded assignments for EECS 16B - Designing Information Devices and Systems II with Anant Sahai for 1 semester. This course walks students through advanced circuitry; this first module of the class introduces students to the frequency domain, a tool critical in circuitry and analyzing many real-world systems. In the next module, students understand stability and controllability of systems, pertinent concepts for robotics. In the final module, students develop fundamental linear algebra building blocks, like SVD, to set them up to implement classification via PCA, a prominent algorithm in machine learning. Current course offering here.

Undergraduate Student Instructor (CS 188)

Undergraduate course, UC Berkeley, EECS, 2022

I taught CS 188 - Introduction to AI with Stuart Russell for 3 different semesters contributing specifically by giving discussion sections, writing exam questions, debugging projects, grading work, and running office hours. This course introduced the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis was on the statistical and decision-theoretic modeling paradigm. Current course offering here.