• Peixin Chang, Shuijing Liu, and Katherine Driggs-Campbell. “Robot Sound Interpretation: Learning Visual-Audio Representations for Voice-Controlled Robots,” 2021.
  [arXiv]
• Zhe Huang, Ruohua Li, Kazuki Shin, and Katherine Driggs-Campbell, “Learning Sparse Interaction Graphs of Partially Detected Pedestrians for Trajectory Prediction,” 2021.
  [arXiv]
• Tianchen Ji, Arun Narenthiran Sivakumar, Girish Chowdhary, and Katherine Driggs-Campbell. “Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion,” 2021.
• Shuijing Liu, Peixin Chang, Haonan Chen, Neeloy Chakraborty, and Katherine Driggs-Campbell. “Learning to Navigate Intersections with Unsupervised Driver Trait Inference,” 2021.
  [arXiv] [website] [youtube]
• Pulkit Katdare, Shuijing Liu, and Katherine Driggs-Campbell. “Off Environment Evaluation Using Convex Risk Minimization,” 2021.
• Aamir Hasan, Pranav Sriram, and Katherine Driggs-Campbell. “Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction,” 2021.
• Masha Itkina, Ye-Ji Mun, Katherine Driggs-Campbell, and Mykel J. Kochenderfer. “Multi-Agent Variational Occlusion Inference Using People as Sensors,” 2021.
  [arXiv] [github]
• Tianchen Ji, Junyi Geng, and Katherine Driggs-Campbell. “Robust Model Predictive Control with State Estimation under Set-Membership Uncertainty,” 2021.
  [arXiv]
• Kyle Brown, Katherine Driggs-Campbell, and Mykel J. Kochenderfer. “A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior,” 2021.
  [arXiv]

recently published

• Shuijing Liu, Peixin Chang, Weihang Liang, Neeloy Chakraborty, and Katherine Driggs-Campbell. “Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning,” In IEEE International Conference on Robotics and Automation (ICRA), 2021.
  [arXiv] [github] [youtube]
• Peter Du and Katherine Driggs-Campbell. “Adaptive Failure Search Using Critical States from Domain Experts.” In IEEE International Conference on Robotics and Automation (ICRA), 2021.
• Yuan Shen, Niviru Wijayaratne, Peter Du, SJ Jiang, and Katherine Driggs-Campbell. “AutoPreview: A Framework for Autopilot Behavior Understanding,” In CHI: Extended Abstracts, 2021.
  [paper]
• Yuan Shen, Niviru Wijayaratne, and Katherine Driggs-Campbell. “Building Mental Models through Preview of Autopilot Behaviors,” in TRAITS Workshop at ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2021.
  [arXiv]

2020

• Zhe Huang, Aamir Hasan, Kazuki Shin, Ruohua Li, and Katherine Driggs-Campbell. “Long-term Pedestrian Trajectory Prediction using Mutable Intention Filter and Warp LSTM,” In IEEE Robotics and Automation Letters, 2020.
  [arXiv]
• Tianchen Ji, Sri Theja Vuppala, Girish Chowdhary, and Katherine Driggs-Campbell. “Multi-Modal Anomaly Detection for Unstructured and Uncertain Environments,” In Conference on Robot Learning (CoRL), 2020.
  [arXiv] [website]
• Peixin Chang, Shuijing Liu, Haonan Chen, and Katherine Driggs-Campbell. “Robot Sound Interpretation: Combining Sight and Sound in Learning-Based Control,” In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020.
  [arXiv] [website]
• Peter Du, Zhe Huang, Tianchen Ji, Tianqi Liu, Ke Xu, Qichao Gao, Hussein Sibai, Katherine Driggs-Campbell, and Sayan Mitra. “Online Monitoring for Safe Pedestrian-Vehicle Interactions,” In IEEE International Conference on Intelligent Transportation Systems (ITSC), 2020.
  [arXiv]
• Carl-Johan Hoel, Katherine Driggs-Campbell, Krister Wolff, Leo Laine, Mykel J. Kochenderfer. “Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving.” In IEEE Transactions on Intelligent Vehicles, 2020.
  [arXiv]

2019

• Peter Du and Katherine Driggs-Campbell. “Finding Diverse Failure Scenarios in Autonomous Systems Using Adaptive Stress Testing,” SAE International Journal of Connected and Automated Vehicles, December 2019.
• Masha Itkina, Katherine Driggs-Campbell, and Mykel J. Kochenderfer. “Dynamic Environment Prediction in Urban Scenes using Recurrent Representation Learning.” In IEEE International Conference on Intelligent Transportation Systems (ITSC), 2019.
  [arXiv]
• Anthony Corso*, Peter Du*, Katherine Driggs-Campbell, and Mykel J. Kochenderfer. “Adaptive Stress Testing with Reward Augmentation for Autonomous Vehicle Validation.” In IEEE International Conference on Intelligent Transportation Systems (ITSC), 2019.
  [arXiv]
• Kunal Menda, Katherine Driggs-Campbell, and Mykel J. Kochenderfer. “EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning.” In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019.
  [arXiv]
• Xiaobai Ma, Katherine Driggs-Campbell, Zongzhang Zhang, and Mykel J. Kochenderfer. “Monte Carlo Tree Search for Policy Optimization.” In International Joint Conference on Artificial Intelligence (IJCAI), 2019.
  [arXiv]
• Michael Kelly, Chelsea Sidrane, Katherine Driggs-Campbell, and Mykel J. Kochenderfer. “HG-DAgger: Interactive Imitation Learning with Human Experts.” In IEEE International Conference on Robotics and Automation (ICRA), 2019.
  [arXiv]
• Raunak Bhattacharyya, Derek J. Phillips, Changliu Liu, Jayesh K. Gupta, Katherine Driggs-Campbell, and Mykel J. Kochenderfer. “Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learning.” In IEEE International Conference on Robotics and Automation (ICRA), 2019.
  [arXiv]

Technical Reports

• Peter Du, Katherine Driggs-Campbell, and Roy Dong. “A Moment-Based Safety Analysis for Stochastic Dynamical Systems,” 2021.
  [arXiv]
• Yuan Shen, and many others. “To Explain or Not to Explain: A Study on the Necessity of Explanations in Autonomous Driving,” 2020.
  [arXiv]