publications

2025

1. Preprint

   

   Unsupervised Discovery of Failure Taxonomies from Deployment Logs

   Aryaman Gupta^*, Yusuf Umut Ciftci^*, and Somil Bansal

   arXiv preprint arXiv:2506.06570, 2025

   arXiv Bib PDF Video Code Website

   @article{gupta2025failure,
     title = {Unsupervised Discovery of Failure Taxonomies from Deployment Logs},
     author = {Gupta, Aryaman and Ciftci, Yusuf Umut and Bansal, Somil},
     journal = {arXiv preprint arXiv:2506.06570},
     year = {2025},
   }

2024

1. ICRA

   

   Detecting and Mitigating System-Level Anomalies of Vision-Based Controllers

   Aryaman Gupta^*, Kaustav Chakraborty^*, and Somil Bansal

   In IEEE International Conference on Robotics and Automation (ICRA), 2024

   arXiv Bib PDF Video Code

   @inproceedings{gupta2024detecting,
     title = {Detecting and Mitigating System-Level Anomalies of Vision-Based Controllers},
     author = {Gupta, Aryaman and Chakraborty, Kaustav and Bansal, Somil},
     booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
     year = {2024},
   }

2. Preprint

   

   Enhancing Safety and Robustness of Vision-Based Controllers via Reachability Analysis

   Kaustav Chakraborty, Aryaman Gupta, and Somil Bansal

   arXiv preprint arXiv:2410.21736, 2024

   arXiv Bib PDF Video Website

   @article{chakraborty2024enhancing,
     title = {Enhancing Safety and Robustness of Vision-Based Controllers via Reachability Analysis},
     author = {Chakraborty, Kaustav and Gupta, Aryaman and Bansal, Somil},
     journal = {arXiv preprint arXiv:2410.21736},
     year = {2024},
   }

3. ASOC

   

   Deep Reinforcement Learning for Sim-to-Real Policy Transfer of VTOL-UAVs Offshore Docking Operations

   Ali Mohamed Ali, Aryaman Gupta, and Hashim A. Hashim

   Applied Soft Computing, 2024

   Abs DOI Bib HTML Code

   This paper proposes a novel Reinforcement Learning (RL) approach for sim-to-real policy transfer of Vertical Take-Off and Landing Unmanned Aerial Vehicles (VTOL-UAV). The proposed approach is designed for VTOL-UAV landing on offshore docking stations in maritime operations. The landing procedure is decomposed into an approach phase, handled by a model-based control scheme, and a landing phase, where DRL agents are trained offline to learn the optimal docking policy. The JONSWAP spectrum model generates a wave model for each episode to enhance policy generalization for sim2real transfer. Numerical experiments show that the PPO agent learns efficient policies to land in uncertain environments, enhancing the likelihood of successful sim-to-real transfer.

   @article{ali2024deep,
     title = {Deep Reinforcement Learning for Sim-to-Real Policy Transfer of VTOL-UAVs Offshore Docking Operations},
     author = {Ali, Ali Mohamed and Gupta, Aryaman and Hashim, Hashim A.},
     journal = {Applied Soft Computing},
     volume = {162},
     pages = {111843},
     year = {2024},
     issn = {1568-4946},
     doi = {10.1016/j.asoc.2024.111843},
   }

4. ANTS

   

   Node Fault Prediction Assisted Small-World IoT Networks Using ML Frameworks: Towards Performance Improvement

   Neha Sharma, Aryaman Gupta, Sivala Deepak, and 1 more author

   In IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), 2024

   Best Paper Bib HTML

   Received the Best Paper Award at IEEE ANTS 2024.

   @inproceedings{sharma2024node,
     title = {Node Fault Prediction Assisted Small-World IoT Networks Using ML Frameworks: Towards Performance Improvement},
     author = {Sharma, Neha and Gupta, Aryaman and Deepak, Sivala and Pandey, Om Jee},
     booktitle = {IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)},
     year = {2024},
   }