Scholar
Letian Chen
Google Scholar ID: SAeHYeQAAAAJ
Waymo
Citations & Impact
Citations
537
H-index
10
i10-index
12
Publications
20
Co-authors
4
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- Safe Learning from Demonstration, Created a new modality for users to specify safe vs. unsafe states for robots via demonstrations, Proposed a novel shielding algorithm, SECURE, that can be applied on policies to enforce customized safety bounds, Tested SECURE on two simulated robotic control tasks and a real robot kitchen cutting task, showed SECURE successfully prevent all unsafe executions; Paper: Learning Interpretable Tree-based Control Policies for Autonomous Driving, Developed interpretable, tree-based continuous-control models that allow gradient updates, Demonstrated strong qualitative and quantitative performance of the proposed model in comparison with black-box neural networks in 10+ driving scenarios, Verified interpretability with user-studies; Paper: Learning from Offline Heterogeneous Demonstrations, Analyzed real Mars rover driving data and identified heterogeneity among rover drivers, Proposed a novel IRL framework, DROID, to accommodate heterogeneous data.
Research Experience
- Research Scientist at Waymo, June 2025 - Now; Research Intern at Waymo, Designed input-output representations for fine-tuning large Vision-Language-Models (VLMs) for the vehicle planning task, Proposed a novel Reinforcement Learning algorithm for fine-tuning VLMs towards planning metrics, Developed training and evaluation pipeline infrastructure of the large VLM, Experiments show significant improvements in target behavior metrics via the proposed method, May 2024 - Dec 2024; Research Intern at Toyota Research Institute, Implemented DIAYN to generate diverse driving policies for autonomous racing, Proposed a novel algorithm, Learn Thy Enemy, to model and leverage opponent information in multi-car racing, Deployed DIAYN and LTE policies on motion simulator hardware, MAY 2023 - AUG 2023; Reinforcement Learning Intern at iRobot Corporation, Identified real-world challenges of Offline Policy Evaluation (OPE) methods, Created an easy-to-use benchmark dataset where real-world challenges present, Proposed an ad-hoc OPE algorithm selection method via validation mechanisms, MAY 2021 - AUG 2021.
Education
- Doctor of Philosophy: Computer Science, Georgia Institute of Technology, Graduated in May 2025; Master of Science: Computer Science, Georgia Institute of Technology, Graduated in May 2020; Bachelor of Science: Computer Science and Technology, Peking University, Graduated in June 2018; Bachelor of Science: Psychology, Peking University, Graduated in June 2018.
Background
- Interested in all kinds of intelligence problems, specifically through the perspective of reinforcement learning. Research enables robots to infer human's intent in Learning from Demonstration settings, teasing out heterogeneity and suboptimality from the demonstrations. Envisions human and machine sharing certain sources of intelligence, including but not limited to reinforcement learning (dopamine system), hierarchical learning (hippocampus), and meta learning (pre-frontal cortex). Aims to reveal the mystery of intelligence through his work and make robot assistance accessible to everyone.
Miscellany
- No additional information provided.
