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@inproceedings{liu2014modeling,
id={C1},
title={Modeling and controller design of cooperative robots in workspace sharing human-robot assembly teams},
author={Liu, Changliu and Tomizuka, Masayoshi},
booktitle={Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on},
pages={1386--1391},
year={2014},
organization={IEEE},
url={https://arxiv.org/abs/1903.02199}
}
@inproceedings{liu2014control,
id={C2},
title={Control in a safe set: Addressing safety in human-robot interactions},
author={Liu, Changliu and Tomizuka, Masayoshi},
booktitle={2014 ASME Dynamic Systems and Control Conference},
year={2014},
organization={ASME}
}
@inproceedings{liu2015safe,
id={C3},
title={Safe exploration: Addressing various uncertainty levels in human robot interactions},
author={Liu, Changliu and Tomizuka, Masayoshi},
booktitle={2015 American Control Conference (ACC)},
pages={465--470},
year={2015},
organization={IEEE}
}
@inproceedings{liu2016algorithmic,
id={C4},
title={Algorithmic safety measures for intelligent industrial co-robots},
author={Liu, Changliu and Tomizuka, Masayoshi},
booktitle={2016 IEEE International Conference on Robotics and Automation (ICRA)},
pages={3095--3102},
year={2016},
organization={IEEE}
}
@inproceedings{liu2016blame,
id={C5},
title={Who to blame? learning and control strategies with information asymmetry},
author={Liu, Changliu and Zhang, Wenlong and Tomizuka, Masayoshi},
booktitle={2016 American Control Conference (ACC)},
pages={4859--4864},
year={2016},
organization={IEEE}
}
@inproceedings{liu2016enabling,
id={C6},
title={Enabling safe freeway driving for automated vehicles},
author={Liu, Changliu and Tomizuka, Masayoshi},
booktitle={2016 American Control Conference (ACC)},
pages={3461--3467},
year={2016},
organization={IEEE}
}
@incollection{liu2017designing,
id={B1},
title={Designing the Robot Behavior for Safe Human--Robot Interactions},
author={Liu, Changliu and Tomizuka, Masayoshi},
booktitle={Trends in Control and Decision-Making for Human--Robot Collaboration Systems},
pages={241--270},
year={2017},
publisher={Springer, Cham}
}
@phdthesis{liu2017designing,
title={Designing robot behavior in human-robot interactions},
author={Liu, Changliu},
year={2017},
school={UC Berkeley}
}
@inproceedings{tang2016robotic,
id={C7},
title={Robotic manipulation of deformable objects by tangent space mapping and non-rigid registration},
author={Tang, Te and Liu, Changliu and Chen, Wenjie and Tomizuka, Masayoshi},
booktitle={2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={2689--2696},
year={2016},
organization={IEEE}
}
@inproceedings{zhan2016non,
id={C8},
title={A non-conservatively defensive strategy for urban autonomous driving},
author={Zhan, Wei and Liu, Changliu and Chan, Ching-Yao and Tomizuka, Masayoshi},
booktitle={2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)},
pages={459--464},
year={2016},
organization={IEEE}
}
@techreport{liu2017robustly,
id={C9},
title={The robustly-safe automated driving system for enhanced active safety},
author={Liu, Changliu and Chen, Jianyu and Nguyen, Trong-Duy and Tomizuka, Masayoshi},
year={2017},
institution={SAE Technical Paper}
}
@inproceedings{liu2017convex,
id={C10},
title={Convex feasible set algorithm for constrained trajectory smoothing},
author={Liu, Changliu and Lin, Chung-Yen and Wang, Yizhou and Tomizuka, Masayoshi},
booktitle={2017 American Control Conference (ACC)},
pages={4177--4182},
year={2017},
organization={IEEE}
}
@inproceedings{zhan2017spatially,
id={C11},
title={Spatially-partitioned environmental representation and planning architecture for on-road autonomous driving},
author={Zhan, Wei and Chen, Jianyu and Chan, Ching-Yao and Liu, Changliu and Tomizuka, Masayoshi},
booktitle={2017 IEEE Intelligent Vehicles Symposium (IV)},
pages={632--639},
year={2017},
organization={IEEE}
}
@inproceedings{liu2017speed,
id={C12},
title={Speed profile planning in dynamic environments via temporal optimization},
author={Liu, Changliu and Zhan, Wei and Tomizuka, Masayoshi},
booktitle={2017 IEEE Intelligent Vehicles Symposium (IV)},
pages={154--159},
year={2017},
organization={IEEE}
}
@inproceedings{liu2017boundary,
id={C13},
title={Boundary layer heuristic for search-based nonholonomic path planning in maze-like environments},
author={Liu, Changliu and Wang, Yizhou and Tomizuka, Masayoshi},
booktitle={2017 IEEE Intelligent Vehicles Symposium (IV)},
pages={831--836},
year={2017},
organization={IEEE}
}
@article{liu2017safe,
id={J1},
title={Safe Robot Navigation Among Moving and Steady Obstacles [Bookshelf]},
author={Liu, Changliu},
journal={IEEE Control Systems Magazine},
volume={37},
number={1},
pages={123--125},
year={2017},
publisher={IEEE}
}
@article{liu2017real,
id={J2},
title={Real time trajectory optimization for nonlinear robotic systems: Relaxation and convexification},
author={Liu, Changliu and Tomizuka, Masayoshi},
journal={Systems \& Control Letters},
volume={108},
pages={56--63},
year={2017},
publisher={North-Holland}
}
@article{liu2018convex,
id={J3},
title={The convex feasible set algorithm for real time optimization in motion planning},
author={Liu, Changliu and Lin, Chung-Yen and Tomizuka, Masayoshi},
journal={SIAM Journal on Control and optimization},
volume={56},
number={4},
pages={2712--2733},
year={2018},
publisher={Society for Industrial and Applied Mathematics}
}
@inproceedings{lin2017real,
id={C14},
title={Real-time collision avoidance algorithm on industrial manipulators},
author={Lin, Hsien-Chung and Liu, Changliu and Fan, Yongxiang and Tomizuka, Masayoshi},
booktitle={2017 IEEE Conference on Control Technology and Applications (CCTA)},
pages={1294--1299},
year={2017},
organization={IEEE}
}
@inproceedings{chen2018foad,
id={C15},
title={FOAD: Fast optimization-based autonomous driving motion planner},
author={Chen, Jianyu and Liu, Changliu and Tomizuka, Masayoshi},
booktitle={2018 Annual American Control Conference (ACC)},
pages={4725--4732},
year={2018},
organization={IEEE}
}
@article{liu2017distributed,
id={J4},
title={Distributed conflict resolution for connected autonomous vehicles},
author={Liu, Changliu and Lin, Chung-Wei and Shiraishi, Shinichi and Tomizuka, Masayoshi},
journal={IEEE Transactions on Intelligent Vehicles},
volume={3},
number={1},
pages={18--29},
year={2017},
publisher={IEEE}
}
@inproceedings{liu2018improving,
id={C16},
title={Improving efficiency of autonomous vehicles by V2V communication},
author={Liu, Changliu and Lin, Chung-Wei and Shiraishi, Shinichi and Tomizuka, Masayoshi},
booktitle={2018 Annual American Control Conference (ACC)},
pages={4778--4783},
year={2018},
organization={IEEE}
}
@inproceedings{liu2018analytically,
id={C17},
title={Analytically Modeling Unmanaged Intersections with Microscopic Vehicle Interactions},
author={Liu, Changliu and Kochenderfer, Mykel J},
booktitle={2018 21st International Conference on Intelligent Transportation Systems (ITSC)},
pages={2352--2357},
year={2018},
organization={IEEE}
}
@article{liu2018analyzing,
title={Analyzing traffic delay at unmanaged intersections},
author={Liu, Changliu and Kochenderfer, Mykel J},
journal={arXiv preprint arXiv:1806.02660},
year={2018}
}
@article{liu2018robot,
title={Robot safe interaction system for intelligent industrial co-robots},
author={Liu, Changliu and Tomizuka, Masayoshi},
journal={arXiv preprint arXiv:1808.03983},
year={2018}
}
@article{liu2018serocs,
title={Serocs: Safe and efficient robot collaborative systems for next generation intelligent industrial co-robots},
author={Liu, Changliu and Tang, Te and Lin, Hsien-Chung and Cheng, Yujiao and Tomizuka, Masayoshi},
journal={arXiv preprint arXiv:1809.08215},
year={2018}
}
@inproceedings{lin2018fast,
id={C18},
title={Fast Robot Motion Planning with Collision Avoidance and Temporal Optimization},
author={Lin, Hsien-Chung and Liu, Changliu and Tomizuka, Masayoshi},
booktitle={2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)},
pages={29--35},
year={2018},
organization={IEEE}
}
@inproceedings{bhattacharyya2019simulating,
id={C19},
title={Simulating emergent properties of human driving behavior using multi-agent reward augmented imitation learning},
author={Bhattacharyya, Raunak P and Phillips, Derek J and Liu, Changliu and Gupta, Jayesh K and Driggs-Campbell, Katherine and Kochenderfer, Mykel J},
booktitle={2019 International Conference on Robotics and Automation (ICRA)},
pages={789--795},
year={2019},
organization={IEEE},
url={https://ieeexplore.ieee.org/abstract/document/9029720}
}
@inproceedings{cheng2019human,
id={C20},
title={Human motion prediction using semi-adaptable neural networks},
author={Cheng, Yujiao and Zhao, Weiye and Liu, Changliu and Tomizuka, Masayoshi},
booktitle={2019 American Control Conference (ACC)},
pages={4884--4890},
year={2019},
organization={IEEE},
url={https://ieeexplore.ieee.org/document/8814980}
}
@inproceedings{si2019agen,
id={C21},
title={Agen: Adaptable generative prediction networks for autonomous driving},
author={Si, Wenwen and Wei, Tianhao and Liu, Changliu},
booktitle={2019 IEEE Intelligent Vehicles Symposium (IV)},
pages={281--286},
year={2019},
organization={IEEE},
url={https://ieeexplore.ieee.org/document/8814238}
}
@inproceedings{xu2019toward,
id={C22},
title={Toward Modularization of Neural Network Autonomous Driving Policy Using Parallel Attribute Networks},
author={Xu, Zhuo and Chang, Haonan and Tang, Chen and Liu, Changliu and Tomizuka, Masayoshi},
booktitle={2019 IEEE Intelligent Vehicles Symposium (IV)},
pages={1400--1407},
year={2019},
organization={IEEE},
url={https://ieeexplore.ieee.org/document/8813861}
}
@inproceedings{wei2019safe,
id={C23},
title={Safe Control Algorithms Using Energy Functions: A Unified Framework, Benchmark, and New Directions},
author={Wei, Tianhao and Liu, Changliu},
booktitle={2019 IEEE 58th Conference on Decision and Control (CDC)},
pages={238-243},
year={2019},
organization={IEEE}
}
@inproceedings{liu2019neuralverification,
id={C24},
title={NeuralVerification.jl: Algorithms for verifying deep neural networks},
author={Liu, Changliu and Arnon, Tomer and Lazarus, Christopher and Kochenderfer, Mykel J},
booktitle={ICLR 2019 Debugging Machine Learning Models Workshop},
year={2019},
url={https://debug-ml-iclr2019.github.io/cameraready/DebugML-19_paper_22.pdf}
}
@inproceedings{abuduweili2019adaptable,
id={C25},
title={Adaptable Human Intention and Trajectory Prediction for Human-Robot Collaboration},
author={Abuduweili, Abulikemu and Li, Siyan and Liu, Changliu},
booktitle={AAAI 2019 Fall Symposium Series, AI for HRI},
year={2019},
url={https://arxiv.org/abs/1909.05089}
}
@article{lin2019graph,
id={J5},
title={Graph-Based Modeling, Scheduling, and Verification for Intersection Management of Intelligent Vehicles},
author={Lin, Yi-Ting and Hsu, Hsiang and Lin, Shang-Chien and Lin, Chung-Wei and Jiang, Iris Hui-Ru and Liu, Changliu},
journal={ACM Transactions on Embedded Computing Systems (TECS)},
volume={18},
number={5s},
pages={1--21},
year={2019},
publisher={ACM New York, NY, USA},
url={https://dl.acm.org/doi/10.1145/3358221}
}
@inproceedings{abuduweili2020robust,
id={C26},
title = {Robust Online Model Adaptation by Extended Kalman Filter with Exponential Moving Average and Dynamic Multi-Epoch Strategy},
author = {Abulikemu Abuduweili and Changliu Liu},
booktitle = {Proceedings of the 2nd Conference on Learning for Dynamics and Control},
pages = {65--74},
year = {2020},
editor = {Alexandre M. Bayen and Ali Jadbabaie and George Pappas and Pablo A. Parrilo and Benjamin Recht and Claire Tomlin and Melanie Zeilinger},
volume = {120},
series = {Proceedings of Machine Learning Research},
address = {The Cloud},
month = {10--11 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v120/abuduweili20a/abuduweili20a.pdf},
url = {http://proceedings.mlr.press/v120/abuduweili20a.html},
abstract = {High fidelity behavior prediction of intelligent agents is critical in many applications. However, the prediction model trained on the training set may not generalize to the testing set due to domain shift and time variance. The challenge motivates the adoption of online adaptation algorithms to update prediction models in real-time to improve the prediction performance. Inspired by Extended Kalman Filter (EKF), this paper introduces a series of online adaptation methods, which are applicable to neural network-based models. A base adaptation algorithm Modified EKF with forgetting factor (MEKF_lambda) is introduced first, followed by exponential moving average filtering techniques. Then this paper introduces a dynamic multi-epoch update strategy to effectively utilize samples received in real time. With all these extensions, we propose a robust online adaptation algorithm: MEKF with Exponential Moving Average and Dynamic Multi-Epoch strategy (MEKF_EMA-DME). The proposed algorithm outperforms existing methods as demonstrated in experiments.},
video = {https://www.youtube.com/watch?v=15vXtd5rg70},
videoembeds = {<iframe width="560" height="315" src="https://www.youtube.com/embed/15vXtd5rg70" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>}
}
@inproceedings{zhao2020experimental,
title={Experimental Evaluation of Human Motion Prediction: Toward Safe and Efficient Human Robot Collaboration},
author={Zhao, Weiye and Sun, Liting and Liu, Changliu and Tomizuka, Masayoshi},
booktitle={American Control Conference},
year={2020},
url={https://arxiv.org/abs/2001.09550},
video = {https://www.youtube.com/watch?v=a47n31DDUiM&t=73s},
videoembeds = {<iframe width="560" height="315" src="https://www.youtube.com/embed/a47n31DDUiM" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>}
}
@article{cheng2020towards,
id={J6},
title={Towards Efficient Human-Robot Collaboration With Robust Plan Recognition and Trajectory Prediction},
author={Cheng, Yujiao and Sun, Liting and Liu, Changliu and Tomizuka, Masayoshi},
journal={IEEE Robotics and Automation Letters},
volume={5},
number={2},
pages={2602--2609},
year={2020},
publisher={IEEE},
url={https://ieeexplore.ieee.org/abstract/document/8990024}
}
@book{liu2019designing,
id={B2},
title={Designing robot behavior in human-robot interactions},
author={Liu, Changliu and Tang, Te and Lin, Hsien-Chung and Tomizuka, Masayoshi},
year={2019},
publisher={CRC Press}
}
@article{liu2020microscopic,
title={A Microscopic Epidemic Model and Pandemic Prediction Using Multi-Agent Reinforcement Learning},
author={Liu, Changliu},
journal={arXiv preprint arXiv:2004.12959},
year={2020}
}
@workshop{ho2020provably,
id={C27},
author = {Cherie Ho and Katherine Shih and Jaskaran Singh Grover and Changliu Liu and Sebastian Scherer},
title = {“Provably Safe” in the Wild: Testing Control Barrier Functions on a Vision-Based Quadrotor in an Outdoor Environment},
booktitle = {Proceedings of RSS '20 2nd Workshop on Robust Autonomy: Safe Robot Learning and Control in Uncertain Real-World Environments},
year = {2020},
month = {July},
}
@inproceedings{grover2020deadlock,
id={C28},
title={Deadlock Analysis and Resolution in Multi-Robot Systems},
author={Grover, Jaskaran and Liu, Changliu and Sycara, Katia},
booktitle={International Workshop on the Algorithmic Foundations of Robotics (WAFR)},
year={2020}
}
@misc{niuwang2020tolerance,
id={C29},
title={Tolerance-guided Policy Learning for Adaptable and Transferrable Delicate Industrial Insertion,},
author={Niu, Boshen and Wang, Chenxi and Liu, Changliu},
year={2020},
journal={CoRL},
url={https://corlconf.github.io/paper_452/},
primaryClass={cs.LG}
}
@proceedings{huang2020multi,
id={C30},
author = {Huang, Jing and Liu, Changliu},
title = "{Multi-Car Convex Feasible Set Algorithm in Trajectory Planning}",
volume = {Volume 1: Adaptive/Intelligent Sys. Control; Driver Assistance/Autonomous Tech.; Control Design Methods; Nonlinear Control; Robotics; Assistive/Rehabilitation Devices; Biomedical/Neural Systems; Building Energy Systems; Connected Vehicle Systems; Control/Estimation of Energy Systems; Control Apps.; Smart Buildings/Microgrids; Education; Human-Robot Systems; Soft Mechatronics/Robotic Components/Systems; Energy/Power Systems; Energy Storage; Estimation/Identification; Vehicle Efficiency/Emissions},
series = {Dynamic Systems and Control Conference},
year = {2020},
month = {10},
abstract = "{Trajectory planning is an essential module for autonomous driving. To deal with multi-vehicle interactions, existing methods follow the prediction-then-plan approaches which first predict the trajectories of others then plan the trajectory for the ego vehicle given the predictions. However, since the true trajectories of others may deviate from the predictions, frequent re-planning for the ego vehicle is needed, which may cause many issues such as instability or deadlock. These issues can be overcome if all vehicles can form a consensus by solving the same multi-vehicle trajectory planning problem. Then the major challenge is how to efficiently solve the multi-vehicle trajectory planning problem in real time under the curse of dimensionality. We introduce a novel planner for multi-vehicle trajectory planning based on the convex feasible set (CFS) algorithm. The planning problem is formulated as a non-convex optimization. A novel convexification method to obtain the maximal convex feasible set is proposed, which transforms the problem into a quadratic programming. Simulations in multiple typical on-road driving situations are conducted to demonstrate the effectiveness of the proposed planning algorithm in terms of completeness and optimality.}",
doi = {10.1115/DSCC2020-3201},
url = {https://doi.org/10.1115/DSCC2020-3201},
note = {V001T10A003},
eprint = {https://asmedigitalcollection.asme.org/DSCC/proceedings-pdf/DSCC2020/84270/V001T10A003/6622145/v001t10a003-dscc2020-3201.pdf},
video = {https://www.youtube.com/watch?v=J2E-db1ahGU},
videoembeds = {<iframe width="560" height="315" src="https://www.youtube.com/embed/J2E-db1ahGU" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>}
}
@proceedings{zhao2020contact,
id={C31},
author = {Zhao, Wei-Ye and He, Suqin and Wen, Chengtao and Liu, Changliu},
title = "{Contact-Rich Trajectory Generation in Confined Environments Using Iterative Convex Optimization}",
volume = {Volume 2: Intelligent Transportation/Vehicles; Manufacturing; Mechatronics; Engine/After-Treatment Systems; Soft Actuators/Manipulators; Modeling/Validation; Motion/Vibration Control Applications; Multi-Agent/Networked Systems; Path Planning/Motion Control; Renewable/Smart Energy Systems; Security/Privacy of Cyber-Physical Systems; Sensors/Actuators; Tracking Control Systems; Unmanned Ground/Aerial Vehicles; Vehicle Dynamics, Estimation, Control; Vibration/Control Systems; Vibrations},
series = {Dynamic Systems and Control Conference},
year = {2020},
month = {10},
abstract = "{Applying intelligent robot arms in dynamic uncertain environments (i.e., flexible production lines) remains challenging, which requires efficient algorithms for real time trajectory generation. The motion planning problem for robot trajectory generation is highly nonlinear and nonconvex, which usually comes with collision avoidance constraints, robot kinematics and dynamics constraints, and task constraints (e.g., following a Cartesian trajectory defined on a surface and maintain the contact). The nonlinear and nonconvex planning problem is computationally expensive to solve, which limits the application of robot arms in the real world. In this paper, for redundant robot arm planning problems with complex constraints, we present a motion planning method using iterative convex optimization that can efficiently handle the constraints and generate optimal trajectories in real time. The proposed planner guarantees the satisfaction of the contact-rich task constraints and avoids collision in confined environments. Extensive experiments on trajectory generation for weld grinding are performed to demonstrate the effectiveness of the proposed method and its applicability in advanced robotic manufacturing.}",
doi = {10.1115/DSCC2020-3208},
url = {https://doi.org/10.1115/DSCC2020-3208},
note = {V002T31A002},
eprint = {https://asmedigitalcollection.asme.org/DSCC/proceedings-pdf/DSCC2020/84287/V002T31A002/6622297/v002t31a002-dscc2020-3208.pdf},
video = {https://www.youtube.com/watch?v=Meo7TnaPido},
videoembeds = {<iframe width="560" height="315" src="https://www.youtube.com/embed/Meo7TnaPido" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>}
}
@INPROCEEDINGS{lin2020dynamic,
id={C32},
author={Lin, Shang-Chien and Hsu, Hsiang and Lin, Yi-Ting and Lin, Chung-Wei and Jiang, Iris Hui-Ru and Liu, Changliu},
booktitle={2020 IEEE Intelligent Vehicles Symposium (IV)},
title={A Dynamic Programming Approach to Optimal Lane Merging of Connected and Autonomous Vehicles},
year={2020},
volume={},
number={},
pages={349-356},
doi={10.1109/IV47402.2020.9304813},
url={https://ieeexplore.ieee.org/document/9304813}
}
@article{abuduweili2020robust,
id={J7},
author = {Abuduweili, Abulikemu and Liu, Changliu},
year = {2020},
month = {11},
pages = {},
title = {Robust nonlinear adaptation algorithms for multitask prediction networks},
volume = {35},
publisher={Wiley Online Library},
journal = {International Journal of Adaptive Control and Signal Processing},
doi = {10.1002/acs.3198}
}
@article{GROVER2020why,
id={C33},
title = {Why Does Symmetry Cause Deadlocks?},
journal = {IFAC-PapersOnLine},
volume = {53},
number = {2},
pages = {9746-9753},
year = {2020},
note = {21th IFAC World Congress},
issn = {2405-8963},
doi = {https://doi.org/10.1016/j.ifacol.2020.12.2644},
url = {https://www.sciencedirect.com/science/article/pii/S2405896320334042},
author = {Jaskaran Grover and Changliu Liu and Katia Sycara},
keywords = {Mobile robots, Multiagent systems, Autonomous robotic systems, Robotics technology, Model predictive, optimization-based control},
abstract = {Collision avoidance for multirobot systems has been studied thoroughly. Recently, control barrier functions (CBFs) have been proposed to mediate between collision avoidance and goal achievement for multiple robots. However, it has been noted that reactive controllers (such as CBFs) are prone to deadlock, an equilibrium that causes the robots to stall before reaching their goals. In this paper, we formally analyze two and three robot systems and discover circumstances under which CBFs cause deadlocks using duality theory. For the two robot system, we consider mutually heterogeneous robots (such as one more vigorous or closer to its goal than the other) and prove that this heterogeneity does not help in preventing deadlock. We then consider three robots, and conclude from these two scenarios that the geometric symmetry resulting from robots’ initial positions and goals constrains CBFs to generate velocities that render deadlock stable. Thus, conferring skewness to the system can help evade deadlock.},
video = {https://www.youtube.com/watch?v=dQ00RrQ1cRg&t=55s},
videoembeds = {<iframe width="560" height="315" src="https://www.youtube.com/embed/dQ00RrQ1cRg" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>}
}
@misc{grover2020feasible,
id={C34},
title={Feasible Region-based Identification Using Duality},
author={Jaskaran Grover and Changliu Liu and Katia Sycara},
year={2020},
eprint={2011.04904},
archivePrefix={arXiv},
journal={ECC},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2011.04904},
video = {https://www.youtube.com/watch?v=T6ZiP22ho9E},
videoembeds = {<iframe width="560" height="315" src="https://www.youtube.com/embed/T6ZiP22ho9E" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>}
}
@misc{jena2020augmenting,
id={C35},
title={Augmenting GAIL with BC for sample efficient imitation learning},
author={Rohit Jena and Changliu Liu and Katia Sycara},
year={2020},
eprint={2001.07798},
archivePrefix={arXiv},
journal={CoRL},
url={https://corlconf.github.io/paper_25/},
primaryClass={cs.LG}
}
@article{grover2020parameter,
title={Parameter Identification for Multirobot Systems Using Optimization Based Controllers (Extended Version)},
author={Grover, Jaskaran Singh and Liu, Changliu and Sycara, Katia},
journal={arXiv:2009.13817},
year={2020}
}
@misc{zhou2021distributed,
id={C36},
title={Distributed Motion Coordination Using Convex Feasible Set Based Model Predictive Control},
author={Hongyu Zhou and Changliu Liu},
year={2021},
eprint={2101.07994},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2101.07994},
journal={ICRA},
primaryClass={cs.RO},
video = {https://www.youtube.com/watch?v=Po792hrkzpY},
videoembeds = {<iframe width="560" height="315" src="https://www.youtube.com/embed/Po792hrkzpY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>}
}
@misc{an2021flexible,
id={C37},
title={Flexible MPC-based Conflict Resolution Using Online Adaptive ADMM},
author={Jerry An and Giulia Giordano and Changliu Liu},
year={2021},
eprint={2103.14118},
archivePrefix={arXiv},
journal={ECC},
url={https://arxiv.org/pdf/2103.14118.pdf},
primaryClass={eess.SY},
video = {https://www.youtube.com/watch?v=lcZOC_lpuqY},
videoembeds = {<iframe width="560" height="315" src="https://www.youtube.com/embed/lcZOC_lpuqY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>}
}
@article{liu2021algorithms,
id={J8},
url = {http://dx.doi.org/10.1561/2400000035},
year = {2021},
volume = {4},
journal = {Foundations and Trends in Optimization},
title = {Algorithms for Verifying Deep Neural Networks},
doi = {10.1561/2400000035},
issn = {2167-3888},
number = {3-4},
pages = {244-404},
author = {Changliu Liu and Tomer Arnon and Christopher Lazarus and Christopher Strong and Clark Barrett and Mykel J. Kochenderfer}
}
@ARTICLE{liu2021human,
id={J9},
author={Liu, Ruixuan and Liu, Changliu},
journal={IEEE Control Systems Letters},
title={Human Motion Prediction Using Adaptable Recurrent Neural Networks and Inverse Kinematics},
year={2021},
volume={5},
number={5},
pages={1651-1656},
doi={10.1109/LCSYS.2020.3042609},
url={https://ieeexplore.ieee.org/document/9281312},
video = {https://www.youtube.com/watch?v=r5TqRTU7lg4},
videoembeds = {<iframe width="560" height="315" src="https://www.youtube.com/embed/r5TqRTU7lg4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>}
}
@article{noren2019safe,
title={Safe Adaptation with Multiplicative Uncertainties Using Robust Safe Set Algorithm},
author={Noren, Charles and Zhao, Weiye and Liu, Changliu},
journal={MECC},
year={2021},
url={https://arxiv.org/abs/1912.09095}
}
@article{gover2021system,
title={System Identification for Safe Controllers using Inverse Optimization},
author={Grover, Jaskaran Singh and Liu, Changliu and Sycara, Katia},
journal={MECC},
year={2021},
url = {https://www.sciencedirect.com/science/article/pii/S2405896321022424},
video = {https://www.youtube.com/watch?v=yusixZPrikk},
videoembeds = {<iframe width="560" height="315" src="https://www.youtube.com/embed/yusixZPrikk" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>}
}
@article{grover2021simultaneously,
title={Simultaneously learning safety margins and task parameters of multirobot systems},
author={Grover, Jaskaran Singh and Liu, Changliu and Sycara, Katia},
journal={RSS BI-MAS Workshop},
year={2021}
}
@article{wei2021online,
title={Online Verification of Deep Neural Networks under Domain or Weight Shift},
author={Wei, Tianhao and Liu, Changliu},
journal={RSS R4P Workshop},
year={2021},
url={https://arxiv.org/abs/2106.12732}
}
@article{liu2021iada,
title={IADA: Iterative Adversarial Data Augmentation Using Formal Verification and Expert Guidance},
author={Liu, Ruixuan and Liu, Changliu},
journal={ICML HIIL Workshop},
year={2021},
url={https://arxiv.org/pdf/2108.06871.pdf}
}
@article{wei2021safe,
title={Safe Control with Neural Network Dynamic Models},
author={Wei, Tianhao and Liu, Changliu},
journal={RSS R4P Workshop},
year={2021},
url={https://arxiv.org/pdf/2110.01110.pdf}
}
@inproceedings{zhao2021model,
title={Model-free Safe Control for Zero-Violation Reinforcement Learning},
author={Zhao, Weiye and He, Tairan and Liu, Changliu},
booktitle={5th Annual Conference on Robot Learning},
year={2021},
url={https://openreview.net/pdf?id=UGp6FDaxB0f}
}
@article{ma2021joint,
title={Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning},
author={Ma, Haitong and Liu, Changliu and Li, Shengbo Eben and Zheng, Sifa and Chen, Jianyu},
journal={MECC SCLU Workshop},
year={2021},
url={https://arxiv.org/pdf/2111.07695.pdf}
}
@inproceedings{zhao2021safe,
title={A Hierarchical Long Short Term Safety Framework for Efficient Robot Manipulation Under Uncertainty},
author={He, Suqin and Zhao, Weiye and Hu, Chuxiong and Zhu, Yu and Liu, Changliu},
booktitle={MECC SCLU Workshop},
year={2021},
url={https://drive.google.com/file/d/1GjHgDfPO23p_3wK38_NNNnfBViCjapvt/view}
}
@article{wang2021hierarchical,
title={Hierarchical Adaptable and Transferable Networks (HATN) for Driving Behavior Prediction},
author={Wang, Letian and Hu, Yeping and Sun, Liting and Zhan, Wei and Tomizuka, Masayoshi and Liu, Changliu},
journal={NeurIPS workshop on Machine Learning for Autonomous Driving},
year={2021},
url={https://arxiv.org/pdf/2111.00788.pdf}
}
@article{wang2021online,
title={Online Adaptation of Neural Network Models by Modified Extended Kalman Filter for Customizable and Transferable Driving Behavior Prediction},
author={Wang, Letian and Hu, Yeping and Liu, Changliu},
journal={AAAI Workshop HCSSL},
year={2021},
url={https://paperswithcode.com/paper/online-adaptation-of-neural-network-models-by}
}
@article{chen2021safe,
title={Safe and Sample-efficient Reinforcement Learning for Clustered Dynamic Environments},
author={Chen, Hongyi and Liu, Changliu},
journal={IEEE Control and Systems Letters},
year={2021}
}
@article{grover2021before,
author = {Jaskaran Grover and Changliu Liu and Katia Sycara},
title ={The before, during, and after of multi-robot deadlock},
journal = {The International Journal of Robotics Research},
year={2021},
url={https://journals.sagepub.com/doi/abs/10.1177/02783649221074718?journalCode=ijra}
}
@article{grover2021parameter,
author = {Jaskaran Grover and Changliu Liu and Katia Sycara},
title ={Parameter Identification for Multirobot Systems Using Optimization-based Controllers},
journal = {International Symposium on Multi-Robot and Multi-Agent Systems (MRS)},
year={2021},
url={https://arxiv.org/pdf/2009.13817.pdf}
}
@inproceedings{liu2022safe2,
author = {Simin Liu and Changliu Liu and John Dolan},
title ={Safe Control Under Input Limits with Neural Control Barrier Functions},
journal = {6th Annual Conference on Robot Learning},
year={2022},
url={https://arxiv.org/abs/2211.11056}
}
@inproceedings{deka2022actor,
author = {Ankur Deka and Changliu Liu and Katia Sycara},
title ={ARC - Actor Residual Critic for Adversarial Imitation Learning},
journal = {6th Annual Conference on Robot Learning},
year={2022},
url={https://arxiv.org/abs/2206.02095#:~:text=Adversarial%20Imitation%20Learning%20(AIL)%20is,Reinforcement%20Learning%20(RL)%20algorithm.}
}
@article{grover2022distributed,
author = {Nishant Mohanty and Jaskaran Grover and Changliu Liu and Katia Sycara},
title ={Distributed Multirobot Control for Non-Cooperative Herding},
journal = {Distributed Autonomous Robotic Systems (DARS)},
year={2022},
url={https://jaskaransgrover.github.io/files/DARS.pdf}
}
@article{liu2022task,
author = {Ruixuan Liu and Rui Chen and Changliu Liu},
title ={Task-agnostic Adaptation for Safe Human-robot Handover},
journal = {4th IFAC Workshop on Cyber-Physical Human Systems},
year={2022},
url={https://arxiv.org/abs/2209.09418}
}
@article{grover2022semantically,
author = {Jaskaran Grover and Yiwei Lyu and Wenhao Luo and Changliu Liu and John Dolan and Katia Sycara},
title ={Semantically-Aware Pedestrian Intent Prediction With Barrier Functions and Mixed-Integer Quadratic Programming},
journal = {4th IFAC Workshop on Cyber-Physical Human Systems},
year={2022},
url={https://webpages.charlotte.edu/wluo4/publications/IFAC_CPHS_2022_Pedestrian_Intent_Prediction.pdf}
}
@article{he2022characterizing,
author = {Haoyu He and Tianhao Wei and Huan Zhang and Changliu Liu and Cheng Tan},
title ={Characterizing Neural Network Verification for Systems with NN4SYSBENCH},
journal = {ICML Workshop on Formal Verification of Machine Learning},
year={2022},
url={https://naizhengtan.github.io/doc/papers/characterizing22haoyu.pdf}
}
@inproceedings{grover2022noncooperative,
author = {Jaskaran Grover and Nishant Mohanty and Wenhao Luo and Changliu Liu and Katia Sycara},
title ={Noncooperative Herding With Control Barrier Functions: Theory and Experiments},
journal = {IEEE Conference on Decision and Control (CDC)},
year={2022},
url={https://arxiv.org/pdf/2204.10945.pdf}
}
@ARTICLE{li2022efficient,
author={Li, Chenran and Trinh, Tu and Wang, Letian and Liu, Changliu and Tomizuka, Masayoshi and Zhan, Wei},
journal={IEEE Robotics and Automation Letters},
title={Efficient Game-Theoretic Planning With Prediction Heuristic for Socially-Compliant Autonomous Driving},
year={2022},
url={https://ieeexplore.ieee.org/document/9830854}}
@inproceedings{pandya2022safe,
url = {https://arxiv.org/abs/2208.01103},
author = {Pandya, Ravi and Liu, Changliu},
title = {Safe and Efficient Exploration of Human Models During Human-Robot Interaction},
year = {2022},
journal={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
video = {https://www.youtube.com/watch?v=XQ8MJiVzSF4},
videoembeds = {<iframe width="560" height="315" src="https://www.youtube.com/embed/XQ8MJiVzSF4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>}
}
@inproceedings{chen2022composable,
url = {https://arxiv.org/abs/2203.03017},
author = {Chen, Rui and Wang, Chenxi and Wei, Tianhao and Liu, Changliu},
title = {A Composable Framework for Policy Design, Learning, and Transfer Toward Safe and Efficient Industrial Insertion},
year = {2022},
journal={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
video = {https://www.youtube.com/watch?v=658vHXza9UA},
videoembeds = {<iframe width="560" height="315" src="https://www.youtube.com/embed/658vHXza9UA" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>}
}
@article{liu2022task,
author = {Ruixuan Liu and Rui Chen and Changliu Liu},
title ={Task-agnostic Adaptation for Safe Human-robot Handover},
journal = {RSS Workshop on Close-Proximity Human-Robot Collaboration},
year={2022},
url={https://arxiv.org/abs/2209.09418}
}
@inproceedings{abuduweili2022an,
title={An Optical Controlling Environment and Reinforcement Learning Benchmarks},
author={ABULIKEMU ABUDUWEILI and Changliu Liu},
booktitle={ICML 2022 2nd AI for Science Workshop},
year={2022},
url={https://openreview.net/forum?id=4iEZLgs_Vxp}
}
@article{zhao2022provably,
title={Provably Safe Tolerance Estimation for Robot Arms via Sum-of-Squares Programming},
author={Zhao, Weiye and He, Suqin and Liu, Changliu},
journal={IEEE Control Systems Letters},
year={2022},
publisher={IEEE},
url={https://ieeexplore.ieee.org/document/9790841},
video={https://www.youtube.com/watch?v=12lgDxBZKHY},
videoembeds={<iframe width="560" height="315" src="https://www.youtube.com/embed/12lgDxBZKHY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>}
}
@INPROCEEDINGS{liu2022jerk,
author={Liu, Ruixuan and Chen, Rui and Sun, Yifan and Zhao, Yu and Liu, Changliu},
booktitle={2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)},
title={Jerk-bounded Position Controller with Real-Time Task Modification for Interactive Industrial Robots},
year={2022},
url={https://ieeexplore.ieee.org/document/9863251},
video = {https://youtu.be/bha6pHhytZA},
videoembeds = {<iframe width="560" height="315" src="https://www.youtube.com/embed/bha6pHhytZA" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>}
}
@INPROCEEDINGS{jessica2022,
author={Jessica Leu and Yujiao Cheng and Changliu Liu and Masayoshi Tomizuka},
booktitle={International Symposium on Flexible Automation},
title={Robust Task Planning for Assembly Lines with Human-Robot Collaboration},
year={2022},
url={https://arxiv.org/abs/2204.07936}}
@inproceedings{liu2022safe,
url = {https://arxiv.org/abs/2204.03038},
author = {Liu, Ruixuan and Chen, Rui and Liu, Changliu},
title = {Safe Interactive Industrial Robots using Jerk-based Safe Set Algorithm},
year = {2022},
journal = {International Symposium on Flexible Automation},
video = {https://www.youtube.com/watch?v=lJvyZ5iRvVQ},
videoembeds = {<iframe width="560" height="315" src="https://www.youtube.com/embed/lJvyZ5iRvVQ" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>}
}
@InProceedings{ma2022joint,
title = {Joint Synthesis of Safety Certificate and Safe Control Policy Using Constrained Reinforcement Learning},
author = {Ma, Haitong and Liu, Changliu and Li, Shengbo Eben and Zheng, Sifa and Chen, Jianyu},
booktitle = {Proceedings of The 4th Annual Learning for Dynamics and Control Conference},
year = {2022},
url = {https://proceedings.mlr.press/v168/ma22a.html},
video = {https://www.youtube.com/watch?v=DZkiF9Nsjmw},
videoembeds = {<iframe width="560" height="315" src="https://www.youtube.com/embed/DZkiF9Nsjmw" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>}
}
@InProceedings{wei2022safe,
title = {Safe Control with Neural Network Dynamic Models},
author = {Wei, Tianhao and Liu, Changliu},
booktitle = {Proceedings of The 4th Annual Learning for Dynamics and Control Conference},
year = {2022},
url = {https://proceedings.mlr.press/v168/wei22a.html},
}
@article{liu2020microscopic,
title={A microscopic epidemic model and pandemic prediction using multi-agent reinforcement learning},
author={Liu, Changliu},
journal={arXiv preprint arXiv:2004.12959},
year={2020}
}
@inproceedings{grover2020deadlock,
title={Deadlock Analysis and Resolution for Multi-Robot Systems},
author={Grover, Jaskaran Singh and Liu, Changliu and Sycara, Katia},
booktitle={International Workshop on the Algorithmic Foundations of Robotics (WAFR)},
year={2020}
}
@inproceedings{zhao2020contact,
title={Contact-rich trajectory generation in confined environments using iterative convex optimization},
author={Zhao, Wei-Ye and He, Suqin and Wen, Chengtao and Liu, Changliu},
booktitle={Dynamic Systems and Control Conference},
volume={84287},
pages={V002T31A002},
year={2020},
organization={American Society of Mechanical Engineers}
}
@article{grover2020parameter,
title={Parameter identification for multirobot systems using optimization based controllers (extended version)},
author={Grover, Jaskaran Singh and Liu, Changliu and Sycara, Katia},
journal={arXiv preprint arXiv:2009.13817},
year={2020}
}
@article{abuduweili2021robust,
title={Robust nonlinear adaptation algorithms for multitask prediction networks},
author={Abuduweili, Abulikemu and Liu, Changliu},
journal={International Journal of Adaptive Control and Signal Processing},
volume={35},
number={3},
pages={314--341},
year={2021}
}
@article{tang2021microscopic,
title={A microscopic pandemic simulator for pandemic prediction using scalable million-agent reinforcement learning},
author={Tang, Zhenggang and Yan, Kai and Sun, Liting and Zhan, Wei and Liu, Changliu},
journal={arXiv preprint arXiv:2108.06589},
year={2021}
}
@article{he2023hierarchical,
title={A hierarchical long short term safety framework for efficient robot manipulation under uncertainty},
author={He, Suqin and Zhao, Weiye and Hu, Chuxiong and Zhu, Yu and Liu, Changliu},
journal={Robotics and Computer-Integrated Manufacturing},
volume={82},
pages={102522},
year={2023},
publisher={Pergamon}
}
@inproceedings{deka2023arc,
title={ARC-Actor Residual Critic for Adversarial Imitation Learning},
author={Deka, Ankur and Liu, Changliu and Sycara, Katia P},
booktitle={Conference on Robot Learning},
pages={1446--1456},
year={2023},
organization={PMLR}
}
@inproceedings{pandya2022safe,
title={Safe and efficient exploration of human models during human-robot interaction},
author={Pandya, Ravi and Liu, Changliu},
booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={6708--6715},
year={2022},
organization={IEEE}
}
@article{li2022efficient,
title={Efficient game-theoretic planning with prediction heuristic for socially-compliant autonomous driving},
author={Li, Chenran and Trinh, Tu and Wang, Letian and Liu, Changliu and Tomizuka, Masayoshi and Zhan, Wei},
journal={IEEE Robotics and Automation Letters},
volume={7},
number={4},
pages={10248--10255},
year={2022},
publisher={IEEE}
}
@article{wang2022social,
title={Social interactions for autonomous driving: A review and perspectives},
author={Wang, Wenshuo and Wang, Letian and Zhang, Chengyuan and Liu, Changliu and Sun, Lijun and others},
journal={Foundations and Trends{\textregistered} in Robotics},
volume={10},
number={3-4},
pages={198--376},
year={2022},
publisher={Now Publishers, Inc.}
}
@article{grover2022control,
title={Control barrier functions-based semi-definite programs (cbf-sdps): Robust safe control for dynamic systems with relative degree two safety indices},
author={Grover, Jaskaran Singh and Liu, Changliu and Sycara, Katia},
journal={arXiv preprint arXiv:2208.12252},