publications

2024

1. 

   Multi-Robot Motion Planning with Diffusion Models

   Yorai Shaoul*, Itamar Mishani*, Shivam Vats*, Jiaoyang Li, and Maxim Likhachev

   arXiv preprint arXiv:2410.03072, 2024

   Bib PDF Code

   @article{shaoul2024multi,
     title = {Multi-Robot Motion Planning with Diffusion Models},
     author = {Shaoul*, Yorai and Mishani*, Itamar and Vats*, Shivam and Li, Jiaoyang and Likhachev, Maxim},
     journal = {arXiv preprint arXiv:2410.03072},
     year = {2024},
   }

2023

1. 

   Efficient Recovery Learning using Model Predictive Meta-Reasoning

   Shivam Vats, Maxim Likhachev, and Oliver Kroemer

   In 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023

   Bib PDF Code Website

   @inproceedings{vats2023efficient,
     title = {Efficient Recovery Learning using Model Predictive Meta-Reasoning},
     author = {Vats, Shivam and Likhachev, Maxim and Kroemer, Oliver},
     booktitle = {2023 IEEE International Conference on Robotics and Automation (ICRA)},
     pages = {7258--7264},
     year = {2023},
     organization = {IEEE},
   }

2022

1. 

   Synergistic scheduling of learning and allocation of tasks in human-robot teams

   Shivam Vats, Oliver Kroemer, and Maxim Likhachev

   In 2022 International Conference on Robotics and Automation (ICRA), 2022

   Finalist for the Outstanding Paper Award in Human-Robot Interaction

   Bib PDF

   @inproceedings{vats2022synergistic,
     title = {Synergistic scheduling of learning and allocation of tasks in human-robot teams},
     author = {Vats, Shivam and Kroemer, Oliver and Likhachev, Maxim},
     booktitle = {2022 International Conference on Robotics and Automation (ICRA)},
     pages = {2789--2795},
     year = {2022},
     organization = {IEEE},
     note = {Finalist for the Outstanding Paper Award in Human-Robot Interaction}
   }

2. 

   Search-based task planning with learned skill effect models for lifelong robotic manipulation

   Jacky Liang, Mohit Sharma, Alex LaGrassa, Shivam Vats, Saumya Saxena, and Oliver Kroemer

   In 2022 International Conference on Robotics and Automation (ICRA), 2022

   Bib PDF Website

   @inproceedings{liang2022search,
     title = {Search-based task planning with learned skill effect models for lifelong robotic manipulation},
     author = {Liang, Jacky and Sharma, Mohit and LaGrassa, Alex and Vats, Shivam and Saxena, Saumya and Kroemer, Oliver},
     booktitle = {2022 International Conference on Robotics and Automation (ICRA)},
     pages = {6351--6357},
     year = {2022},
     organization = {IEEE},
   }

2020

1. 

   Learning to use adaptive motion primitives in search-based planning for navigation

   Raghav Sood, Shivam Vats, and Maxim Likhachev

   In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020

   Bib PDF

   @inproceedings{sood2020learning,
     title = {Learning to use adaptive motion primitives in search-based planning for navigation},
     author = {Sood, Raghav and Vats, Shivam and Likhachev, Maxim},
     booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
     pages = {6923--6929},
     year = {2020},
     organization = {IEEE},
   }

2017

1. 

   Learning to avoid local minima in planning for static environments

   Shivam Vats, Venkatraman Narayanan, and Maxim Likhachev

   In Proceedings of the International Conference on Automated Planning and Scheduling, 2017

   Bib PDF

   @inproceedings{vats2017learning,
     title = {Learning to avoid local minima in planning for static environments},
     author = {Vats, Shivam and Narayanan, Venkatraman and Likhachev, Maxim},
     booktitle = {Proceedings of the International Conference on Automated Planning and Scheduling},
     volume = {27},
     pages = {572--576},
     year = {2017},
   }

2. 

   SymPy: symbolic computing in Python

   Aaron Meurer, and  others

   PeerJ Computer Science, 2017

   Bib PDF

   @article{meurer2017sympy,
     title = {SymPy: symbolic computing in Python},
     author = {Meurer, Aaron and others},
     journal = {PeerJ Computer Science},
     volume = {3},
     pages = {e103},
     year = {2017},
     publisher = {PeerJ Inc.},
   }