Hi there, and welcome to my personal website! My name’s Nishanth and I’m a researcher and engineer focused on AI and ML applied to robotics. I also moonlight as a writer.
Presently, I’m a Ph.D. student in the LIS Group within MIT CSAIL. I’m officially advised by Leslie Kaelbling and Tomás Lozano-Pérez and work within the greater Embodied Intelligence community of research at CSAIL. I’m extremely grateful for support from the NSF Graduate Research Fellowship.
In the recent past, I graduated from Brown University with honors in Computer Engineering. As an undergrad, I was fortunate to work on exciting AI + Robotics research under Brown’s BigAI initiative advised by Professors Stefanie Tellex, George Konidaris and Michael Littman. I spent time as a Research Intern at Uber ATG and Vicarious AI, and even had the opportunity to collaborate with researchers at MERL.
Outside of all this, I like to lift heavy things, read, play basketball, philosophize, cook, and sometimes make progress on various sci-fi novels/short-stories that I’ve been writing for longer than I care to admit.
If you’re interested in learning more about me or reading some of my writing, check out my resume, blog, fiction writing, or social links in the website footer. If you’d like to get in contact, check out this page here.
Recent News
- I’ll be at RSS 2024 presenting our work on planning to practice for mobile manipulation, and also presenting new work on LLM’s + TAMP in the lifelong learning workshop! Very much looking forward to meeting new people; reach out if you’re interested in chatting!
- I’m spending Summer 2024 at NVIDIA’s Seattle Robotics Research Lab! Reach out if you’re in the area and want to chat about anything robotics/AI!
- New paper from my internship at the Boston Dynamics AI Institute is out! Check it out here. [Update] This work has been accepted at RSS 2024!
- Released a new blog post on the lab website that provides an intro to bilevel planning; check it out here!
- Our work on learning efficient operators for TAMP has been accepted at CoRL 2023! Come check out the poster and chat with me if you’ll be attending!
- Our work on learning efficient operators for TAMP is a spotlight talk at the RSS Workshop on Learning for TAMP! (Update: the work was also honored with the ‘Best Paper’ award!)