Publications

recently published

  • Shuijing Liu, Aamir Hasan, Kaiwen Hong, Runxuan Wang, Peixin Chang, Zachary Mizrachi, Justin Lin, D Livingston McPherson, Wendy A Rogers, and Katherine Driggs-Campbell. “A Dialogue-Based Robot for Assistive Navigation with Visual Language Grounding,” IEEE Robotics and Automation Letters (RA-L), 2024.
    [arXiv] [website]
  • Zhe Huang, Hongyu Chen, and Katherine Driggs-Campbell. “Neural Informed RRT* with Point-based Network Guidance for Optimal Sampling-based Path Planning,” IEEE International Conference on Robotics and Automation (ICRA), 2024.
    [arXiv] [code]

2023

  • Sourya Basu*, Pulkit Katdare*, Prasanna Sattigeri, Vijil Chenthamarakshan, Katherine Rose Driggs-Campbell, Payel Das, Lav R Varshney “Efficient Equivariant Transfer Learning from Pretrained Models,” Conference on Neural Information Processing Systems (NeurIPS), 2023.
    [arXiv]
  • Haonan Chen, Yilong Niu, Kaiwen Hong, Shuijing Liu, Yixuan Wang, Yunzhu Li, and Katherine Driggs-Campbell “Predicting Object Interactions with Behavior Primitives: An Application in Stowing Tasks,” Conference on Robot Learning (CoRL), 2023.
    Best Paper Award Finalist.
    [arXiv]
  • Peixin Chang, Shuijing Liu, Tianchen Ji, Neeloy Chakraborty, Kaiwen Hong, and Katherine Driggs-Campbell “A Data-Efficient Visual-Audio Representation with Intuitive Fine-tuning for Voice-Controlled Robots,” Conference on Robot Learning (CoRL), 2023.
    [pdf]
  • Pulkit Katdare, Nan Jiang, and Katherine Driggs-Campbell. “Marginalized Importance Sampling for Off-Environment Policy Evaluation,” Conference on Robot Learning (CoRL), 2023.
    [arXiv]
  • Yu-Chen Chang, Nitish Gandi, Kazuki Shin, Ye-Ji Mun, Katherine Driggs-Campbell, and Joohyung Kim. “Specifying Target Objects in Robot Teleoperation Using Speech and Natural Eye Gaze,” IEEE International Conference on Humanoid Robots (Humanoids), 2023.
  • Aamir Hasan, Neeloy Chakraborty, Haonan Chen, Jung-Hoon Cho, Cathy Wu, and Katherine Driggs-Campbell. “PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems,” IEEE Intelligent Transportation Systems Conference (ITSC), 2023.
    [arXiv] [website] [code]
  • Andre Schreiber, Tianchen Ji, D Livingston McPherson, and Katherine Driggs-Campbell. “An Attentional Recurrent Neural Network for Occlusion-Aware Proactive Anomaly Detection in Field Robot Navigation,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023.
    [arXiv] [code]
  • W.J. Wagner, A. Soylemezoglu, D. Nottage, K. Driggs-Campbell. “Online Soil Property Estimation for Autonomous Earthmoving Using Physics-Infused Neural Networks,” Conference of the International Society for Terrain-Vehicle Systems (ISTVS), 2023.
    [arXiv]
  • Yixuan Wang*, Yunzhu Li*, Katherine Driggs-Campbell, Fei-Fei Li, and Jiajun Wu. “Dynamic-Resolution Model Learning for Object Pile Manipulation,” Robotics: Science and Systems (RSS), 2023.
    [arXiv]
  • Ye-Ji Mun, Masha Itkina, Shuijing Liu, and Katherine Driggs-Campbell. “Occlusion-Aware Crowd Navigation Using People as Sensors,” IEEE International Conference on Robotics and Automation (ICRA), 2023.
    [arXiv] [youtube] [code]
  • Shahab Sagheb, Ye-Ji Mun, Neema Ahmadian, Benjamin Christie, Andrea Bajcsy, Katherine Driggs-Campbell, and Dylan Losey. “Towards Robots that Influence Humans over Long-Term Interaction,” IEEE International Conference on Robotics and Automation (ICRA), 2023.
    [arXiv] [youtube]
  • Zhe Huang*, Ye-Ji Mun*, Xiang Li, Yixing Xie, Ninghan Zhong, Weihang Liang, Junyi Geng, Tan Chen, and Katherine Driggs-Campbell. “Hierarchical Intention Tracking for Robust Human-Robot Collaboration in Industrial Assembly Tasks,” IEEE International Conference on Robotics and Automation (ICRA), 2023.
    [arXiv]
  • Shuijing Liu, Peixin Chang, Zhe Huang, Neeloy Chakraborty, Kaiwen Hong, Weihang Liang, D. Livingston McPherson, Junyi Geng, and Katherine Driggs-Campbell. “Socially-Aware Robot Crowd Navigation with Attention-Based Interaction Graph,” IEEE International Conference on Robotics and Automation (ICRA), 2023.
    [arXiv] [website] [code]
  • Neeloy Chakraborty, Aamir Hasan, Shuijing Liu, Tianchen Ji, Weihang Liang, D. Livingston McPherson, and Katherine Driggs-Campbell. “Structural Attention-based Recurrent Variational Autoencoder for Highway Vehicle Anomaly Detection,” ACM International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2023.
    [arXiv] [website] [code]
  • Peixin Chang, Shuijing Liu, and Katherine Driggs-Campbell. “Learning Visual-Audio Representations for Voice-Controlled Robots,” IEEE International Conference on Robotics and Automation (ICRA), 2023.
    [arXiv]

2022

  • Yuan Shen, Shanduojiao Jiang, Yanlin Chen, Eileen Yang, Xilun Jin, Yuliang Fan, and Katherine Driggs-Campbell. “To Explain or Not to Explain: A Study on the Necessity of Explanations in Autonomous Driving,” NeurIPS Workshop on Progress and Challenges in Building Trustworthy Embodied AI (TEA), 2022.
    [arXiv] Best Paper Award Winner.
  • Tianchen Ji, Junyi Geng, and Katherine Driggs-Campbell. “Robust Model Predictive Control with State Estimation under Set-Membership Uncertainty,” CDC 2022.
    [arXiv]
  • W. Jacob Wagner*, Katherine Driggs-Campbell, and Ahmet Soylemezoglu. “Model Learning and Predictive Control for Autonomous Obstacle Reduction via Bulldozing,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022.
  • Peter Du, Katherine Driggs-Campbell, and Roy Dong. “Improving the Feasibility of Moment-Based Safety Analysis for Stochastic Dynamics,” To appear in Transactions on Automatic Control (TAC), 2022.
    [arXiv]
  • Yuan Shen, Niviru Wijayaratne, Pranav Sriram, Aamir Hasan, Peter Du, and Katherine Driggs-Campbell. “CoCAtt: A Cognitive-Conditioned Driver Attention Dataset,” IEEE Intelligent Transportation Systems Conference (ITSC), 2022.
    [arXiv]
  • Haonan Chen, Tianchen Ji, Shuijing Liu, and Katherine Driggs-Campbell. “Combining Model-Based Controllers and Generative Adversarial Imitation Learning for Traffic Simulation,” IEEE Intelligent Transportation Systems Conference (ITSC), 2022.
  • Megan A. Bayles, Travis Kadylak, Shuijing Liu, Aamir Hasan, Weihang Liang, Kaiwen Hong, Kathrine Driggs-Campbell, and Wendy A. Rogers. “An Interdisciplinary Approach: Potential for Robotic Support to Address Wayfinding Barriers Among Persons with Visual Impairments,” Human Factors and Ergonomics Society’s International Annual Meeting (HFES), 2022.
  • Abhi Kamboj, Tianchen Ji, and Katherine Driggs-Campbell. “Examining Audio Communication Mechanisms for Supervising Fleets of Agricultural Robots,” IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2022.
  • Tianchen Ji, Roy Dong, and Katherine Driggs-Campbell. “Traversing Supervisor Problem: An Approximately Optimal Approach to Multi-Robot Assistance,” Robotics: Science and Systems (RSS), 2022.
    [arXiv]
  • Aamir Hasan, Neeloy Chakraborty, Cathy Wu, and Katherine Driggs-Campbell. “Towards Co-operative Congestion Mitigation,” ICRA Workshop on Shared Autonomy in Physical Human-Robot Interaction: Adaptability and Trust, 2022.
  • Tan Chen, Zhe Huang, James Motes, Junyi Geng, Quang Minh Ta, Holly Dinkel, Hameed Abdul-Rashid, Jessica Myers, Ye-Ji Mun, Wei-che Lin, Yuan-yung Huang, Sizhe Liu, Marco Morales, Nancy M. Amato, Katherine Driggs-Campbell, Timothy Bretl. “Industrial Collaborative Assembly Project: Lessons in Research and Collaboration,” Spotlight presentation at ICRA 2022 Workshop on Collaborative Robots and the Work of the Future, 2022.
  • Shuijing Liu*, Aamir Hasan*, Kaiwen Hong, Chun-Kai Yao, Justin Lin, Weihang Liang, Megan A. Bayles, Wendy A. Rogers, and Katherine Driggs-Campbell. “Designing a Wayfinding Robot for People with Visual Impairments,” ICRA Workshop on Intelligent Control Methods and Machine Learning Algorithms for Human-Robot Interaction and Assistive Robotics, 2022.
  • Masha Itkina, Ye-Ji Mun, Katherine Driggs-Campbell, and Mykel J. Kochenderfer. “Multi-Agent Variational Occlusion Inference Using People as Sensors,” In IEEE International Conference on Robotics and Automation (ICRA), 2022.
    [arXiv] [github]
  • Aamir Hasan, Pranav Sriram, and Katherine Driggs-Campbell. “Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction,” In IEEE International Conference on Robotics and Automation (ICRA), 2022.
  • Shuijing Liu, Peixin Chang, Haonan Chen, Neeloy Chakraborty, and Katherine Driggs-Campbell. “Learning to Navigate Intersections with Unsupervised Driver Trait Inference,” In IEEE International Conference on Robotics and Automation (ICRA), 2022.
    [arXiv] [website] [youtube]
  • Pulkit Katdare, Shuijing Liu, and Katherine Driggs-Campbell. “Off Environment Evaluation Using Convex Risk Minimization,” In IEEE International Conference on Robotics and Automation (ICRA), 2022.
  • Tianchen Ji, Arun Narenthiran Sivakumar, Girish Chowdhary, and Katherine Driggs-Campbell. “Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion,” IEEE Robotics and Automation Letters (RA-L), 2022.
    To be presented at In IEEE International Conference on Robotics and Automation (ICRA), 2022.

2021

  • Zhe Huang, Ruohua Li, Kazuki Shin, and Katherine Driggs-Campbell, “Learning Sparse Interaction Graphs of Partially Detected Pedestrians for Trajectory Prediction,” in IEEE Robotics and Automation Letters (RA-L), 2021.
    To be presented at In IEEE International Conference on Robotics and Automation (ICRA), 2022.
    [arXiv]
  • 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.
    [arXiv]
  • 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]
  • Kyle Brown, Katherine Driggs-Campbell, and Mykel J. Kochenderfer. “A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior,” Technical Report, 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]

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