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Conference Articles

K.R. Zentner , Ryan C. Julian, Brian Ichter, Gaurav S. Sukhatme, Conditionally combining robot skills using large language models, 2023, (ICRA 2024) [URL]

K.R. Zentner , Ujjwal Puri, Yulun Zhang, Ryan Julian, Gaurav S. Sukhatme, Efficient multi-task learning via iterated single-task transfer, 2022, (Oral Presentation at IROS 2022) [URL]

K.R. Zentner , Ujjwal Puri, Zhehui Huang, Gaurav S. Sukhatme, Guaranteed trust region optimization via two-phase KL penalization, 2023, (Under Preparation) [URL]

Shashank Hegde, Sumeet Batra, K.R. Zentner , Gaurav S. Sukhatme, Generating behaviorally diverse policies with latent diffusion models, 2023, (Poster at NeurIPS 2023) [URL]

Other Research

K.R. Zentner , doexp: A simple experiment runner, 2023, [URL]

K.R. Zentner , Language-World: A language and robotics benchmark based on Meta-World, 2023, [URL]

K.R. Zentner , Ryan Julian, Ujjwal Puri, Yulun Zhang, Gaurav S. Sukhatme, Towards exploiting geometry and time for fast off-distribution adaptation in multi-task robot learning, 2021, (Poster at the NeurIPS 2020 Workshop: Challenges of Real World Reinforcement Learning and NeurIPS 2020 Workshop: Offline RL) [URL]

K.R. Zentner , Ujjwal Puri, Yulun Zhang, Ryan Julian, Gaurav S. Sukhatme, Multi-task learning via iterated single-task transfer, 2022, (Poster Presentation at The Multi-disciplinary Conference on Reinforcement Learning and Decision Making 2022) [URL]

K.R. Zentner , Ryan Julian, Ujjwal Puri, Yulun Zhang, Gaurav S. Sukhatme, A simple approach to continual learning by transferring skill parameters, 2021, [URL]

The garage contributors, Garage: A toolkit for reproducible reinforcement learning research, 2019, (Contributor 2018-2023) [URL]

Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Avnish Narayan, Hayden Shively, Adithya Bellathur, Karol Hausman, Chelsea Finn, Sergey Levine, MetaWorld, 2019, (Lead Maintainer 2021-2023) [URL]

Career History

In short: I attended UC Berkeley (BS EECS, 2015), worked for Google (X, Search, Brain), and now attend USC (PhD CS, 2024).

University of Southern California

I’m currently a PhD student in computer science, advised by Gaurav Sukhatme. I’ve published several papers, which you can see here. I’ve worked as a Research Assistant for much of my time at USC in the Robotic Embedded Systems Laboratory.

Google Brain (now Google DeepMind)

I interned at Google Brain in Summer 2022, which eventually lead to a paper on controlling robots via a fine-tuned interpretation of plans generated by a Large Language Model.

I worked on the Google Search Infrastructure team from 2016 to 2018. My largest accomplishment was a client-side caching layer which reduced bandwidth used for JavaScript by 50% on Tier 1 searches, while measurably reducing latency and reducing global power usage by roughly 10MW.

Google Robotics / X (now Everyday Robots)

I interned with this group in Spring 2016, when the Everyday Robots project was first started. As my intern project, I created the first web-based data visualizer (“v0”). I designed the overall architecture and implemented video and compressed voxel visualization.

University of California, Berkeley

I received a Bachelors of Science in Electrical Engineering and Computer Science from UC Berkeley. While I was there, I worked as a TA for The Beauty and Joy of Computing, lead the GamesCrafters research group, and was heavily involved in PiE Robotics.

PiE Robotics

I took on several volunteer roles at PiE, participating in over a hundred work session and helping to host dozens of on-campus events.

My first year with them, I helped develop their in-house open-hardware motor controller, the Grizzly Bear. This allowed students to use several more motors on their robots. I also developed their field control system, Forsetti.

My second year with PiE I helped lead Control System Next, an ambitious project to build an extremely inexpensive yet modular real-time robotic control system.

My third year with PiE I served as Engineering Coordinator, where I managed roughly 40 volunteer staff for a semester. I also taught at a high school in Oakland once a week as a PREP mentor.


I interned at Mozilla in the summer of 2015. I implemented an early version of the CSS Containment specification, and recommended ways for the specification to be improved. I gained in-depth knowledge about HTML layout, which lead me to contribute the original flexbox parser to Servo.

I also fixed a subtle bug in HTML table layout that had been present since HTML tables were first implemented.

Wind River / VxWorks CoreOS

I interned with the VxWorks CoreOS team in the summer of 2013. I added directory support to the NetDRV file system, fixed a few bugs, and improved the POSIX compatibility layer.


I intern here in the summer of 2014. I implemented a C parser and binding generator for running TD-CDMA protocol tests, a GUI to a set of version control scripts, and a test automation server.