Research
I’m broadly interested in machine learning and robotics. I hope to build intelligent agents that can generalize to a wide range of real-world scenarios.
|
|
PlayFusion: Skill Acquisition via Diffusion from Language-Annotated Play
Lili Chen*,
Shikhar Bahl*,
Deepak Pathak
Conference on Robot Learning (CoRL), 2023.
pdf / website
We present a language-conditioned diffusion model which can learn visuomotor policies from language-annotated play data.
|
|
Affordances from Human Videos as a Versatile Representation for Robotics
Shikhar Bahl*,
Russell Mendonca*,
Lili Chen,
Unnat Jain,
Deepak Pathak
Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
pdf / website / code
We train a visual affordance model on human videos to estimate how a human is likely to interact with objects and deploy this model in robotic control tasks.
|
|
Decision Transformer: Reinforcement Learning via Sequence Modeling
Lili Chen*,
Kevin Lu*,
Aravind Rajeswaran,
Kimin Lee,
Aditya Grover,
Michael Laskin,
Pieter Abbeel,
Aravind Srinivas*,
Igor Mordatch*
Neural Information Processing Systems (NeurIPS), 2021.
pdf / website /
code / video (by Yannic Kilcher)
We propose to replace traditional offline RL algorithms with a simple transformer model trained on sequences of returns, states, and actions with an autoregressive prediction loss.
|
|
State Entropy Maximization with Random Encoders for Efficient Exploration
Younggyo Seo*,
Lili Chen*,
Jinwoo Shin,
Honglak Lee,
Pieter Abbeel,
Kimin Lee
International Conference on Machine Learning (ICML), 2021.
pdf / website / code
We tackle exploration for high-dimensional observation spaces using a k-NN state entropy estimator in the low-dimensional representation space of a randomly intialized CNN.
|
|
Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings
Lili Chen,
Kimin Lee,
Aravind Srinivas,
Pieter Abbeel
Neural Information Processing Systems (NeurIPS), 2021.
pdf / code
We present a compute- and memory-efficient modification of off-policy visual RL methods by freezing lower layers of CNN encoders and storing low-dimensional embeddings.
|
|
Ising Model Optimization Problems on a FPGA Accelerated Restricted Boltzmann Machine
Saavan Patel,
Lili Chen,
Philip Canoza,
Sayeef Salahuddin
arXiv preprint, 2020.
pdf
We demonstrate usage of RBMs to solve NP-Hard problems efficiently by mapping the RBM onto a reconfigurable FPGA.
|
Teaching
I hope to improve the accessibility of computer science education, at all levels.
|
(CMU) 10-716: Advanced Machine Learning (PhD)
Teaching Assistant: Spring 2024
|
(CMU) 16-831: Introduction to Robot Learning
Teaching Assistant: Fall 2023
|
(UC Berkeley) CS 70: Discrete Mathematics and Probability Theory
Head Teaching Assistant: Spring 2021, Fall 2020
Teaching Assistant: Spring 2020
Reader: Fall 2019
|
(UC Berkeley) Computer Science Mentors [Website]
Mentor: Fall 2019, Spring 2019
|
(UC Berkeley) Berkeley ANova [Website]
Mentor: Spring 2019, Fall 2018, Spring 2018
|
Website template from here.
|
|