Kevin Lin

MS Computer Science at Stanford, advised by Jeannette Bohg; previously, EECS at UC Berkeley. I did research at Waabi, advised by Raquel Urtasun, and at Berkeley Artificial Intelligence Research with members of Pieter Abbeel's Robot Learning Lab. I also did research with Somil Bansal.

If you're interested in collaborating or getting into robotics, feel free to email!

Email  /  LinkedIn  /  Github  /  Google Scholar

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I'm interested in developing general purpose robots.

Previously, I have leveraged foundation models for robust and verified long horizon planning for robot manipulation from natural language instructions. Currently, I'm developing a general purpose robotic manipulation framework to enable three nines of reliability on tasks when provided a few demonstrations.

In the future, I aim to develop a general purpose robotics foundation model that can be finetuned for specific robot embodiments and settings. I plan to achieve this goal by leveraging a diverse range of data sources --- real world robot data, simulation data, human videos --- and using methods from machine learning, vision, graphics, and robotics.

Consistency Policy: Accelerated Visuomotor Policies via Consistency Distillation
Aaditya Prasad, Kevin Lin, Linqi Zhou, Jeannette Bohg

In submission: Robotics Science and Systems, 2024

We propose Consistency Policy, a faster and similarly powerful alternative to Diffusion Policy for learning visuomotor robotic manipulation policies. We compare Consistency Policy with Diffusion Policy and other related speed-up methods across 6 simulation tasks as well as one real-world task where we demonstrate inference on a laptop GPU. For all these tasks, Consistency Policy speeds up inference by an order of magnitude compared to the fastest alternative method and maintains competitive success rates.

DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
Alexander Khazatsky*, Karl Pertsch*,..., Kevin Lin, ..., Sergey Levine, Chelsea Finn

In submission: Robotics Science and Systems, 2024

We introduce DROID, the most diverse robot manipulation dataset to date. It contains 76k demonstration trajectories or 350 hours of interaction data, collected across 564 scenes and 84 tasks by 50 data collectors in North America, Asia, and Europe over the course of 12 months. We demonstrate that training with DROID leads to policies with higher performance and improved generalization ability. We open source the full dataset, policy learning code, and a detailed guide for reproducing our robot hardware setup.

Text2Motion: From Natural Language Instructions to Feasible Plans
Kevin Lin, Christopher Agia, Toki Migimatsu, Marco Pavone, Jeannette Bohg

Autonomous Robots, 2023 (Special Issue: Large Language Models in Robotics)
ICRA 2023 Workshop on Pretraining for Robotics

We propose Text2Motion, a language-based planning framework enabling robots to solve sequential manipulation tasks that require long-horizon reasoning. Given a natural language instruction, our framework constructs both a task- and motion-level plan that is verified to reach inferred symbolic goals.

Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Open X-Embodiment Collaboration

IEEE International Conference on Robotics and Automation (ICRA), 2024 (Best Paper Award Finalist)

We introduce the Open X-Embodiment Dataset, the largest robot learning dataset to date with 1M+ real robot trajectories, spanning 22 robot embodiments. We train large, transformer-based policies on the dataset (RT-1-X, RT-2-X) and show that co-training with our diverse dataset substantially improves performance.

Partial-View Object View Synthesis via Filtering Inversion
Fan-Yun Sun, Jonathan Tremblay, Valts Blukis, Kevin Lin, Danfei Xu, Boris Ivanovic, Peter Karkus, Stan Birchfield, Dieter Fox, Ruohan Zhang, Yunzhu Li, Jiajun Wu, Marco Pavone, Nick Haber

International Conference on 3D Vision, 2024 (Oral Presentation)

We propose a framework that combines the strengths of generative modeling and network finetun-ing to generate photorealistic 3D renderings of real-world objects from sparse and sequential RGB inputs.

Active View Planning for Radiance Fields
Kevin Lin, Brent Yi

Robotics Science and Systems: Workshop on Implicit Representations for Robot Manipulation, 2022 (Spotlight Presentation)

We motivate, discuss, and present a study on the problem of view planning for radiance fields. We introduce a benchmark, active-3d-gym, for evaluating view planning algorithms for radiance field reconstructions and propose a simple solution to the view planning problem based on radiance field ensembles.

Combining optimal control and learning for autonomous aerial navigation in novel indoor environments
Kevin Lin, Brian Huo, Megan Hu

Arxiv, 2021

We study how aerial robots can autonomously learn to navigate safely in novel indoor environments by combining optimal control and learning techniques. We train our agent entirely in simulation and demonstrate generalization on novel indoor scenes.

Beliefs and Level-k Reasoning in Traffic
E Vinitsky, A Filos, Kevin Lin, N Liu, N Lichtle, A Dragan, A Bayen, R McAllister, J Foerster

NeurIPS: Workshop on Emergent Communication, 2020

Occlusions present a major obstacle to guaranteeing safety in autonomous driving. Our key insight is that, sometimes, AV can get information about occluded regions by inferring over the actions of other agents on the road. We demonstrate that AVs can use this inferred data and level-K reasoning to avoid collisions with occluded pedestrians and drive in a pro-social manner.

Tesroo: A Redesigned Vacuum Robot

Won 1st place in UC Berkeley robotics course's final project

Envisioned and built a prototype vision-only robot vacuum using Visual SLAM to compete with LiDAR based Roomba models.

FLOW: A deep reinforcement learning framework for mixed autonomy traffic

Developed an open-source tool for applying ML techniques to autonomous vehicle driving policy discovery


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