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How to Evaluate Proving Grounds for Self-Driving? A Quantitative Approach

Rui Chen, Mansur Arief, Weiyang Zhang, Ding Zhao. In IEEE Transactions on Intelligent Transportation Systems (T-ITS) (In Review), 2019

This work evaluates the effectiveness of connected and automated vehicle testing grounds in terms of their capability to accomodate real-world driving scenarios, which are extracted from naturalistic driving data via Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM).

GRIP: generative robust inference and perception for semantic robot manipulation in adversarial environments

Xiaotong Chen, Rui Chen, Zhiqiang Sui, Zhefan Ye, Yanqi Liu, R. Iris Bahar, Odest Chadwicke Jenkins. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (To Appear). 2019

We develop a two-stage object detection and pose estimation system that aims to combine relative strength of discriminative CNNs and generative inference methods to achieve robust estimation under adversarial environments.

Active Learning for Risk-Sensitive Inverse Reinforcement Learning

Rui Chen, Wenshuo Wang, Zirui Zhao, Ding Zhao. In International Conference on Robotics and Automation (ICRA) (In Review), 2020

Risk-sensitive inverse reinforcement learning provides an general model to capture how human assess the distribution of a stochastic outcome when the true distribution is unknown (ambiguous). This work enables an RS-IRL learner to actively query expert demonstrations for faster risk envelope approximation.



Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.