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            <p> Check out the <a href="https://scholar.google.com/citations?user=joR1Z4UAAAAJ&hl=en&oi=ao" target="_blank">Google Scholar</a> page for a full and up-to-date publication list. <sup>*</sup> denotes equal contribution.
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            <heading>Preprints</heading>
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            <papertitle>WoCoCo: Learning Whole-Body Humanoid Control with Sequential Contacts</papertitle>
            <br>
            Chong Zhang<sup>*</sup>, Wenli Xiao<sup>*</sup>, Tairan He, Guanya Shi
            <br>
            <a href="https://arxiv.org/abs/2406.06005" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://lecar-lab.github.io/wococo/" target="_blank"><i class="fas fa-globe"></i> website</a> &nbsp
            <a href="https://spectrum.ieee.org/video-friday-drone-vs-flying-canoe" target="_blank"><i class="fas fa-newspaper"></i> IEEE Spectrum</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: WoCoCo is a simple and task-agnostic skill learning framework without any motion priors, by decomposing a sequence of contacts.
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            <papertitle>OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning</papertitle>
            <br>
            Tairan He<sup>*</sup>, Zhengyi Luo<sup>*</sup>, Xialin He<sup>*</sup>, Wenli Xiao, Chong Zhang, Weinan Zhang, Kris Kitani, Changliu Liu, Guanya Shi
            <br>
            <a href="https://arxiv.org/abs/2406.08858" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://omni.human2humanoid.com/" target="_blank"><i class="fas fa-globe"></i> website</a> &nbsp
            <a href="https://cmu.box.com/s/kmayzq5ax2rxvwn97s0hzz0aq5vws9io" target="_blank"><i class="fas fa-database"></i> dataset</a> &nbsp
            <a href="https://spectrum.ieee.org/video-friday-drone-vs-flying-canoe" target="_blank"><i class="fas fa-newspaper"></i> IEEE Spectrum</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: OmniH2O provides a universal whole-body humanoid control interface that enables diverse teleoperation and autonomy methods.
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            <papertitle>Model-Based Diffusion for Trajectory Optimization</papertitle>
            <br>
            Chaoyi Pan<sup>*</sup>, Zeji Yi<sup>*</sup>, Guanya Shi, Guannan Qu
            <br>
            <a href="https://drive.google.com/file/d/1kPjD79Cfr9spWulWNVFMRHqTE-mjbGAp/view?usp=sharing" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://lecar-lab.github.io/mbd/" target="_blank"><i class="fas fa-globe"></i> website</a> &nbsp
            <a href="https://github.com/LeCAR-Lab/model-based-diffusion" target="_blank"><i class="fas fa-code"></i> code</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: MBD is a diffusion-based traj optimization method that directly computes the score function using models without any external data. 
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                <img src='assets/H2O.gif' width="85%">
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            <papertitle>Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation</papertitle>
            <br>
            Tairan He<sup>*</sup>, Zhengyi Luo<sup>*</sup>, Wenli Xiao, Chong Zhang, Kris Kitani, Changliu Liu, Guanya Shi 
            <br>
            <a href="https://arxiv.org/abs/2403.04436" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://human2humanoid.com/" target="_blank"><i class="fas fa-globe"></i> website</a> &nbsp
            <a href="https://spectrum.ieee.org/video-friday-human-to-humanoid" target="_blank"><i class="fas fa-newspaper"></i> IEEE Spectrum</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: H2O enables real-time whole-body teleoperation of a full-sized humanoid to perform tasks like pick and place, walking, kicking, boxing, etc.
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            <papertitle>Guardians as You Fall: Active Mode Transition for Safe Falling</papertitle>
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            Yikai Wang, Mengdi Xu, Guanya Shi, Ding Zhao
            <br>
            <a href="https://arxiv.org/abs/2310.04828" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://sites.google.com/view/guardians-as-you-fall/" target="_blank"><i class="fas fa-globe"></i> website</a> &nbsp
            <a href="https://github.com/ykwang20/Guardians_as_You_Fall" target="_blank"><i class="fas fa-code"></i> code</a> &nbsp
            <a href="https://youtu.be/e0ORrUjrncc?si=oQwIA9tf8VHGk8D6" target="_blank"><i class="fas fa-video"></i> video</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: GYF is a safe falling and recovery framework that can actively tumble and recover to stable modes to reduce damage.
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            <heading>2024</heading>
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            <papertitle>Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion</papertitle>
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            Tairan He<sup>*</sup>, Chong Zhang<sup>*</sup>, Wenli Xiao, Guanqi He, Changliu Liu, Guanya Shi 
            <br>
            <em>Robotics: Science and Systems (RSS)</em>, 2024
            <br>
            <a href="https://arxiv.org/abs/2401.17583" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://agile-but-safe.github.io/" target="_blank"><i class="fas fa-globe"></i> website</a> &nbsp 
            <a href="https://github.com/LeCAR-Lab/ABS" target="_blank"><i class="fas fa-code"></i> code</a> &nbsp 
            <a href="https://spectrum.ieee.org/video-friday-agile-but-safe" target="_blank"><i class="fas fa-newspaper"></i> IEEE Spectrum</a> &nbsp
            <a href="https://www.ri.cmu.edu/collision-free-high-speed-robots/" target="_blank"><i class="fas fa-newspaper"></i> CMU News</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: ABS enables fully onboard, agile (>3m/s), and collision-free locomotion for quadrupedal robots in cluttered environments.
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            <papertitle>Model Predictive Control for Aggressive Driving Over Uneven Terrain</papertitle>
            <br>
            Tyler Han, Alex Liu, Anqi Li, Alex Spitzer, Guanya Shi, Byron Boots
            <br>
            <em>Robotics: Science and Systems (RSS)</em>, 2024
            <br>
            <a href="https://arxiv.org/abs/2311.12284" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://sites.google.com/cs.washington.edu/off-road-mpc" target="_blank"><i class="fas fa-globe"></i> website</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: We design a terrain-aware MPC framework that enables agile driving over uneven offroad geometries such as hills, banks, and ditches.
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            <papertitle>CoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Design</papertitle>
            <br>
            Zeji Yi<sup>*</sup>, Chaoyi Pan<sup>*</sup>, Guanqi He, Guannan Qu, Guanya Shi
            <br>
            <em>Conference on Learning for Dynamics and Control (L4DC)</em>, 2024
            <br>
            <a href="https://arxiv.org/abs/2401.07369" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://lecar-lab.github.io/CoVO-MPC/" target="_blank"><i class="fas fa-globe"></i> website</a> &nbsp
            <a href="https://github.com/LeCAR-Lab/CoVO-MPC" target="_blank"><i class="fas fa-code"></i> code</a> 
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: We quantify the convergence rate of sampling-based MPC, and design a practical and effective algorithm CoVO-MPC with optimal rate.
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                <img src='publications/2024_HMAC.png ' width="85%">
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            <papertitle>Hierarchical Meta-learning-based Adaptive Controller</papertitle>
            <br>
            Fengze Xie, Guanya Shi,  Michael O'Connell, Yisong Yue, Soon-Jo Chung
            <br>
            <em>International Conference on Robotics and Automation (ICRA)</em>, 2024
            <br>
            <a href="https://arxiv.org/abs/2311.12367" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://sites.google.com/view/hmacproject" target="_blank"><i class="fas fa-globe"></i> website</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: HMAC handles both manageable and latent disturbances with hierarchical iterative learning and smoothed streaming meta-learning.
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            <papertitle>Safe Deep Policy Adaptation</papertitle>
            <br>
            Wenli Xiao<sup>*</sup>, Tairan He<sup>*</sup>, John Dolan, Guanya Shi
            <br>
            <em>International Conference on Robotics and Automation (ICRA)</em>, 2024
            <br>
            <a href="https://arxiv.org/abs/2310.08602" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://sites.google.com/view/safe-deep-policy-adaptation" target="_blank"><i class="fas fa-globe"></i> website</a> &nbsp
            <a href="https://youtu.be/PkyRzlRQVbE?si=B3guhmFEJyFhhgiS" target="_blank"><i class="fas fa-video"></i> video</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: SafeDPA jointly tackles the problems of policy adaptation and safe reinforcement learning, under unseen disturbances in the real world.
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                <img src='publications/2024_DMPO.png' width="85%">
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            <papertitle>Deep Model Predictive Optimization</papertitle>
            <br>
            Jacob Sacks, Rwik Rana, Kevin Huang, Alex Spitzer, Guanya Shi, Byron Boots
            <br>
            <em>International Conference on Robotics and Automation (ICRA)</em>, 2024
            <br>            
            <a href="https://arxiv.org/abs/2310.04590" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://sites.google.com/uw.edu/dmpo" target="_blank"><i class="fas fa-globe"></i> website</a> &nbsp
            <a href="https://github.com/jisacks/dmpo" target="_blank"><i class="fas fa-code"></i> code</a> 
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: DMPO learns the inner-loop optimizer of sampling-based MPC directly via experience, outperforming MPC and end-to-end RL baselines.
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                <img src='publications/2024_aerial_interaction.gif' width="85%">
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            <papertitle>Aerial Interaction with Tactile Sensing</papertitle>
            <br>
            Xiaofeng Guo, Guanqi He, Mohammadreza Mousaei, Junyi Geng, Guanya Shi, Sebastian Scherer
            <br>
            <em>International Conference on Robotics and Automation (ICRA)</em>, 2024
            <br>            
            <a href="https://arxiv.org/abs/2310.00142" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://sites.google.com/view/aerial-system-gelsight" target="_blank"><i class="fas fa-globe"></i> website</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: We introduce a new aerial manipulation system that leverages tactile feedback for accurate contact force control and texture detection.
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            <heading>2023</heading>
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            <papertitle>Optimal Exploration for Model-based RL in Nonlinear Systems</papertitle>
            <br>
            Andrew Wagenmaker, Guanya Shi, Kevin Jamieson
            <br>
            <em>Neural Information Processing Systems (NeurIPS)</em>, 2023
            <p style="color: orange; margin: 0px 0;">(Spotlight, 3.1%)</p>
            <a href="https://arxiv.org/abs/2306.09210" target="_blank"><i class="far fa-file"></i> paper</a> 
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: Not all model parameters are equally important. We develop an instance-optimal exploration algorithm for MBRL in nonlinear systems.
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                <img src='publications/2023_active_learning.png' width="85%">
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            <papertitle>Active Representation Learning for General Task Space with Applications in Robotics</papertitle>
            <br>
            Yifang Chen, Yingbing Huang, Simon S. Du, Kevin Jamieson, Guanya Shi
            <br>
            <em>Neural Information Processing Systems (NeurIPS)</em>, 2023
            <br>
            <a href="https://arxiv.org/abs/2306.08942" target="_blank"><i class="far fa-file"></i> paper</a> 
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: Inspired by robotics applications, we study algorithms for active representation learning with continuous task parametrization.
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            <papertitle>DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control</papertitle>
            <br>
            Kevin Huang, Rwik Rana, Alexander Spitzer, Guanya Shi, Byron Boots
            <br>
            <em>Conference on Robot Learning (CoRL)</em>, 2023
            <p style="color: orange; margin: 0px 0;">(Oral presentation, 6.6%)</p>
            <a href="https://openreview.net/pdf?id=XEw-cnNsr6" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://sites.google.com/view/deep-adaptive-traj-tracking" target="_blank"><i class="fas fa-globe"></i> website</a> &nbsp
            <a href="https://github.com/KevinHuang8/DATT" target="_blank"><i class="fas fa-code"></i> code</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: DATT can precisely track arbitrary, potentially infeasible trajectories in the presence of large disturbances.
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            <papertitle>CAJun: Continuous Adaptive Jumping using a Learned Centroidal Controller</papertitle>
            <br>
            Yuxiang Yang, Guanya Shi, Xiangyun Meng, Wenhao Yu, Tingnan Zhang, Jie Tan, Byron Boots
            <br>
            <em>Conference on Robot Learning (CoRL)</em>, 2023
            <br>
            <a href="https://arxiv.org/abs/2306.09557" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://youtu.be/uWofjeirdEY" target="_blank"><i class="fas fa-video"></i> video</a> &nbsp
            <a href="https://yxyang.github.io/cajun/" target="_blank"><i class="fas fa-globe"></i> website</a> &nbsp
            <a href="https://github.com/yxyang/cajun" target="_blank"><i class="fas fa-code"></i> code</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: CAJun is a hierarchical learning and control framework that enables legged robots to jump continuously with adaptive distances.
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            <papertitle>Leveraging Predictions in Power System Frequency Control: an Adaptive Approach</papertitle>
            <br>
            Wenqi Cui, Guanya Shi, Yuanyuan Shi, Baosen Zhang
            <br>
            <em>IEEE Conference on Decision and Control (CDC)</em>, 2023
            <br>
            <a href="https://arxiv.org/abs/2305.12044" target="_blank"><i class="far fa-file"></i> paper</a> 
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: We combine adaptive nonlinear control and neural control for frequency restoration in power systems.
            </p>
        </td>
    </tr>
    </table>

    <br>
    <br>

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td width="100%" valign="middle">
            <heading>2022</heading>
        </td>
    </tr>
    </table>

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td style="width:35%; vertical-align:middle; padding-right: 20px;">
            <img src='publications/2022_neural_fly.gif' width="100%">
        </td>
        <td style="width:65%; vertical-align:middle">
            <papertitle>Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds</papertitle>
            <br>
            Michael O'Connell<sup>*</sup>, Guanya Shi<sup>*</sup>, Xichen Shi, Kamyar Azizzadenesheli, Animashree Anandkumar, Yisong Yue, Soon-Jo Chung
            <br>
            <em>Science Robotics</em>
            <br>
            <a href="https://arxiv.org/abs/2205.06908" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://youtu.be/TuF9teCZX0U" target="_blank"><i class="fas fa-video"></i> video</a> &nbsp
            <a href="https://www.caltech.edu/about/news/rapid-adaptation-of-deep-learning-teaches-drones-to-survive-any-weather" target="_blank"><i class="fas fa-newspaper"></i> Caltech news</a> &nbsp
            <a href="https://youtu.be/R1S5BnKgJxs" target="_blank"><i class="fas fa-newspaper"></i> Reuters</a> &nbsp
            <a href="https://www.cnn.com/videos/business/2022/05/31/caltech-neural-fly-drones-in-strong-wind-orig-ht.cnn-business/video/playlists/business-tech/" target="_blank"><i class="fas fa-newspaper"></i> CNN</a> &nbsp
            <a href="https://github.com/aerorobotics/neural-fly" target="_blank"><i class="fas fa-code"></i> code</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: Neural-Fly uses adaptive control to online fine-tune a meta-pretrained DNN representation, enabling rapid adaptation in strong winds. 
            </p>
        </td>
    </tr>
    </table>

    <br>

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td style="width:35%; vertical-align:middle; padding-right: 20px;">
            <div class="image-container">
                <img src="publications/2022_soco_delay_nonlinear.png" width="90%">
            </div>
        </td>
        <td style="width:65%; vertical-align:middle">
            <papertitle>Online Optimization with Feedback Delay and Nonlinear Switching Cost</papertitle>
            <br>
            Weici Pan, Guanya Shi, Yiheng Lin, Adam Wierman
            <br>
            <em>Proceedings of the ACM on Measurement and Analysis of Computing Systems (SIGMETRICS)</em>
            <br>
            <a href="https://dl.acm.org/doi/abs/10.1145/3508037" target="_blank"><i class="far fa-file"></i> paper</a> 
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: We propose a new online optimization setting with delay and nonlinear switching cost, and provide compeititve algorithms.
            </p>
        </td>
    </tr>
    </table>

    <br>

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td style="width:35%; vertical-align:middle; padding-right: 20px;">
            <div class="image-container">
                <img src="publications/2022_robustness_consistency.png" width="100%">
            </div>
        </td>
        <td style="width:65%; vertical-align:middle">
            <papertitle>Robustness and Consistency in Linear Quadratic Control with Predictions</papertitle>
            <br>
            Tongxin Li<sup>*</sup>, Ruixiao Yang<sup>*</sup>, Guannan Qu, Guanya Shi, Chenkai Yu, Adam Wierman, Steven Low
            <br>
            <em>Proceedings of the ACM on Measurement and Analysis of Computing Systems (SIGMETRICS)</em>
            <br>
            <a href="https://dl.acm.org/doi/abs/10.1145/3508038" target="_blank"><i class="far fa-file"></i> paper</a> 
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: For online control with noisy predictions, we design an algorithm to optimally balance robustness and consistency (performance if no noise).
            </p>
        </td>
    </tr>
    </table>

    <br>

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td style="width:35%; vertical-align:middle; padding-right: 20px;">
            <div class="image-container">
                <img src="publications/2022_delayed_imperfect_information.png" width="100%">
            </div>
        </td>
        <td style="width:65%; vertical-align:middle">
            <papertitle>Competitive Control with Delayed Imperfect Information</papertitle>
            <br>
            Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue, Adam Wierman
            <br>
            <em>American Control Conference (ACC)</em>, 2022
            <br>
            <a href="https://ieeexplore.ieee.org/abstract/document/9867421" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://www.gshi.me/blog/CompetitiveMPC/" target="_blank"><i class="fas fa-file-alt"></i> blog</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: We study MPC's dynamic regret and competitive ratio in the presence of prediction inaccuracy and feedback delay.
            </p>
        </td>
    </tr>
    </table>

    <br>
    <br>

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td width="100%" valign="middle">
            <heading>2021</heading>
        </td>
    </tr>
    </table>

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td style="width:35%; vertical-align:middle; padding-right: 20px;">
            <div class="image-container">
                <img src="publications/2021_perturbation_mpc.png" width="100%">
            </div>
        </td>
        <td style="width:65%; vertical-align:middle">
            <papertitle>Perturbation-based Regret Analysis of Predictive Control in LTV Systems</papertitle>
            <br>
            Yiheng Lin<sup>*</sup>, Yang Hu<sup>*</sup>, Guanya Shi<sup>*</sup>, Haoyuan Sun<sup>*</sup>, Guannan Qu<sup>*</sup>, Adam Wierman
            <br>
            <em>Neural Information Processing Systems (NeurIPS)</em>, 2021
            <p style="color: orange; margin: 0px 0;">(Spotlight, 2.9%)</p>
            <a href="https://proceedings.neurips.cc/paper/2021/hash/298f587406c914fad5373bb689300433-Abstract.html" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://www.gshi.me/blog/CompetitiveMPC/" target="_blank"><i class="fas fa-file-alt"></i> blog</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: We prove MPC's dynamic regret and competitive ratio exponentially improve as its prediction gets longer, in LTV systems.
            </p>
        </td>
    </tr>
    </table>

    <br>

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td style="width:35%; vertical-align:middle; padding-right: 20px;">
            <div class="image-container">
                <img src="publications/2021_OMAC.gif" width="100%">
            </div>
        </td>
        <td style="width:65%; vertical-align:middle">
            <papertitle>Meta-Adaptive Nonlinear Control: Theory and Algorithms</papertitle>
            <br>
            Guanya Shi, Kamyar Azizzadenesheli, Michael O'Connell, Soon-Jo Chung, Yisong Yue
            <br>
            <em>Neural Information Processing Systems (NeurIPS)</em>, 2021
            <br>
            <a href="https://arxiv.org/abs/2106.06098" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://github.com/GuanyaShi/Online-Meta-Adaptive-Control" target="_blank"><i class="fas fa-code"></i> code</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: We present an online multi-task learning approach for adaptive nonlinear control with non-asymptotic guarantees.
            </p>
        </td>
    </tr>
    </table>

    <br>

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td style="width:35%; vertical-align:middle; padding-right: 20px;">
            <div class="image-container">
                <img src="publications/2021_fast_uq.png" width="85%">
            </div>
        </td>
        <td style="width:65%; vertical-align:middle">
            <papertitle>Fast Uncertainty Quantification for Deep Object Pose Estimation</papertitle>
            <br>
            Guanya Shi, Yifeng Zhu, Jonathan Tremblay, Stan Birchfield, Fabio Ramos, Animashree Anandkumar, Yuke Zhu
            <br>
            <em>International Conference on Robotics and Automation (ICRA)</em>, 2021
            <br>
            <a href="https://arxiv.org/abs/2011.07748" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://sites.google.com/view/fastuq" target="_blank"><i class="fas fa-globe"></i> website</a> &nbsp
            <a href="https://developer.nvidia.com/blog/nvidia-research-fast-uncertainty-quantification-for-deep-object-pose-estimation/" target="_blank"><i class="fas fa-file-alt"></i> blog</a> &nbsp
            <a href="https://github.com/NVlabs/DOPE-Uncertainty" target="_blank"><i class="fas fa-code"></i> code</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: We develop a simple and efficient UQ method for 6-DoF pose estimation, and apply it in real-world grasping tasks.
            </p>
        </td>
    </tr>
    </table>

    <br>

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td style="width:35%; vertical-align:middle; padding-right: 20px;">
            <img src='publications/2021_neural_swarm.gif' width="100%">
        </td>
        <td style="width:65%; vertical-align:middle">
            <papertitle>Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms Using Learned Interactions</papertitle>
            <br>
            Guanya Shi, Wolfgang Hönig, Xichen Shi, Yisong Yue, Soon-Jo Chung
            <br>
            <em>IEEE Transactions on Robotics</em>
            <br>
            <a href="https://arxiv.org/abs/2012.05457" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://youtu.be/Y02juH6BDxo" target="_blank"><i class="fas fa-video"></i> video</a> &nbsp
            <a href="https://www.caltech.edu/about/news/machine-learning-helps-robot-swarms-coordinate" target="_blank"><i class="fas fa-newspaper"></i> Caltech news</a> &nbsp
            <a href="https://news.yahoo.com/caltech-drone-swarm-ai-174642584.html" target="_blank"><i class="fas fa-newspaper"></i> Yahoo! news</a> &nbsp
            <a href="https://github.com/aerorobotics/neural-swarm" target="_blank"><i class="fas fa-code"></i> code</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: Neural-Swarm is a learning-based controller and planner for close-proximity flight of heterogeneous multirotor swarms.
            </p>
        </td>
    </tr>
    </table>

    <br>
    <br>    

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td width="100%" valign="middle">
            <heading>2020</heading>
        </td>
    </tr>
    </table>

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td style="width:35%; vertical-align:middle; padding-right: 20px;">
            <div class="image-container">
                <img src="publications/2020_info_snoc.png" width="80%">
            </div>
        </td>
        <td style="width:65%; vertical-align:middle">
            <papertitle>Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems</papertitle>
            <br>
            Yashwanth Kumar Nakka, Anqi Liu, Guanya Shi, Animashree Anandkumar, Yisong Yue, Soon-Jo Chung
            <br>
            <em>IEEE Robotics and Automation Letters (RA-L)</em>
            <br>
            <a href="https://arxiv.org/abs/2005.04374" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://yashwanthnakka.com/trajectory-optimization-for-safe-exploration/" target="_blank"><i class="fas fa-file-alt"></i> blog</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: We derive an iterative algorithm to solve information-cost stochastic nonlinear optimal control problems for safe episodic learning.
            </p>
        </td>
    </tr>
    </table>

    <br>

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td style="width:35%; vertical-align:middle; padding-right: 20px;">
            <img src='publications/2020_robust_regression.png' width="100%">
        </td>
        <td style="width:65%; vertical-align:middle">
            <papertitle>Robust Regression for Safe Exploration in Control</papertitle>
            <br>
            Anqi Liu, Guanya Shi, Soon-Jo Chung, Animashree Anandkumar, Yisong Yue
            <br>
            <em>Conference on Learning for Dynamics and Control (L4DC)</em>, 2020
            <br>
            <a href="https://arxiv.org/pdf/1906.05819" target="_blank"><i class="far fa-file"></i> paper</a> 
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: We derive generalization bounds under domain shift and connect them with safety bounds in control, for end-to-end safe explorations.
            </p>
        </td>
    </tr>
    </table>

    <br>

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td style="width:35%; vertical-align:middle; padding-right: 20px;">
            <img src='publications/2020_mpc.png' width="100%">
        </td>
        <td style="width:65%; vertical-align:middle">
            <papertitle>The Power of Predictions in Online Control</papertitle>
            <br>
            Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue, Adam Wierman
            <br>
            <em>Neural Information Processing Systems (NeurIPS)</em>, 2020
            <br>
            <a href="https://arxiv.org/abs/2006.07569" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://videos.neurips.cc/search/power%20of%20predictions%20in%20online%20control/video/slideslive-38936069" target="_blank"><i class="fas fa-video"></i> video</a> &nbsp
            <a href="https://www.gshi.me/blog/CompetitiveMPC/" target="_blank"><i class="fas fa-file-alt"></i> blog</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: We give the first non-asymptotic guarantee for MPC. MPC's dynamic regret exponentially decreases as its prediction gets longer.
            </p>
        </td>
    </tr>
    </table>

    <br>

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td style="width:35%; vertical-align:middle; padding-right: 20px;">
            <div class="image-container">
                <img src="publications/2020_optimistic_robd.png" width="90%">
            </div>
        </td>
        <td style="width:65%; vertical-align:middle">
            <papertitle>Online Optimization with Memory and Competitive Control</papertitle>
            <br>
            Guanya Shi<sup>*</sup>, Yiheng Lin<sup>*</sup>, Soon-Jo Chung, Yisong Yue, Adam Wierman
            <br>
            <em>Neural Information Processing Systems (NeurIPS)</em>, 2020
            <br>
            <a href="https://arxiv.org/abs/2002.05318" target="_blank"><i class="far fa-file"></i> paper</a> 
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: We show competitive algorithms for a new class of online optimization problems, with applications in online competitive control.
            </p>
        </td>
    </tr>
    </table>

    <br>

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td style="width:35%; vertical-align:middle; padding-right: 20px;">
            <div class="image-container">
                <img src="publications/2020_neural_swarm.png" width="90%">
            </div>
        </td>
        <td style="width:65%; vertical-align:middle">
            <papertitle>Neural-Swarm: Decentralized Close-Proximity Multirotor Control Using Learned Interactions</papertitle>
            <br>
            Guanya Shi, Wolfgang Hönig, Yisong Yue, Soon-Jo Chung
            <br>
            <em>International Conference on Robotics and Automation (ICRA)</em>, 2020
            <br>
            <a href="https://ieeexplore.ieee.org/abstract/document/9196800" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://youtu.be/v4j-9pH11Q8" target="_blank"><i class="fas fa-video"></i> video</a> &nbsp
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: We deploy Deep Sets to learn complex interactions between multirotors, for decentralized close-proximity control.
            </p>
        </td>
    </tr>
    </table>

    <br>
    <br>

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td width="100%" valign="middle">
            <heading>2019</heading>
        </td>
    </tr>
    </table>

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td style="width:35%; vertical-align:middle; padding-right: 20px;">
            <img src='publications/2019_neural_lander.gif' width="100%">
        </td>
        <td style="width:65%; vertical-align:middle">
            <papertitle>Neural Lander: Stable Drone Landing Control Using Learned Dynamics</papertitle>
            <br>
            Guanya Shi<sup>*</sup>, Xichen Shi<sup>*</sup>, Michael O'Connell<sup>*</sup>, Rose Yu, Kamyar Azizzadenesheli, Animashree Anandkumar, Yisong Yue, Soon-Jo Chung
            <br>
            <em>International Conference on Robotics and Automation (ICRA)</em>, 2019
            <br>
            <a href="https://arxiv.org/abs/1811.08027" target="_blank"><i class="far fa-file"></i> paper</a> &nbsp
            <a href="https://youtu.be/FLLsG0S78ik" target="_blank"><i class="fas fa-video"></i> video</a> &nbsp
            <a href="https://www.caltech.edu/about/news/neural-lander-uses-ai-land-drones-smoothly" target="_blank"><i class="fas fa-newspaper"></i> Caltech homepage news</a> &nbsp
            <a href="https://github.com/GuanyaShi/neural_lander_sim_1d" target="_blank"><i class="fas fa-code"></i> code</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: Spectrally normalized deep learning and nonlinear control enable provably stable agile drone landing.
            </p>
        </td>
    </tr>
    </table>

    <br>
    <br>

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td width="100%" valign="middle">
            <heading>2018</heading>
        </td>
    </tr>
    </table>

    <table width="880" border="0" align="center" cellspacing="0" cellpadding="0">
    <tr>
        <td style="width:35%; vertical-align:middle; padding-right: 20px;">
            <img src='publications/2018_car_following.png' width="100%">
        </td>
        <td style="width:65%; vertical-align:middle">
            <papertitle>Car-following Method Based on Inverse Reinforcement Learning for Autonomous Vehicle Decision-making</papertitle>
            <br>
            Hongbo Gao, Guanya Shi, Guotao Xie, Bo Cheng
            <br>
            <em>International Journal of Advanced Robotic Systems</em>
            <br>
            <a href="https://journals.sagepub.com/doi/full/10.1177/1729881418817162" target="_blank"><i class="far fa-file"></i> paper</a>
            <p style="margin-top: 5px"><i class="fas fa-comment-dots"></i> TL;DR: We use inverse RL to learn reward models for human-like autonomous car following.
            </p>
        </td>
    </tr>
    </table>

    <br>
    <br>
    <br>

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