publications

2023

1. 

   Learning Performance Graphs From Demonstrations via Task-Based Evaluations

   Aniruddh G. Puranic, Jyotirmoy V. Deshmukh, and Stefanos Nikolaidis

   IEEE Robotics and Automation Letters, 2023

   arXiv Bib HTML Supp

   @article{PDN23,
     author = {Puranic, Aniruddh G. and Deshmukh, Jyotirmoy V. and Nikolaidis, Stefanos},
     journal = {IEEE Robotics and Automation Letters},
     title = {Learning Performance Graphs From Demonstrations via Task-Based Evaluations},
     year = {2023},
     volume = {8},
     number = {1},
     pages = {336-343},
     doi = {10.1109/LRA.2022.3226072},
   }

2022

1. HSCC

   Poster Abstract: Learning from Demonstrations with Temporal Logics

   Aniruddh Puranic, Jyotirmoy Deshmukh, and Stefanos Nikolaidis

   In 25th ACM International Conference on Hybrid Systems: Computation and Control, 2022

   Abs

   Learning-from-demonstrations (LfD) is a popular paradigm to obtain effective robot control policies for complex tasks via reinforcement learning without the need to explicitly design reward functions. However, it is susceptible to imperfections in demonstrations and also raises concerns of safety and interpretability in the learned control policies. To address these issues, we propose to use Signal Temporal Logic (STL) to express high-level robotic tasks and use its quantitative semantics to evaluate and rank the quality of demonstrations. Temporal logic-based specifications allow us to create non-Markovian rewards, and are also capable of defining interesting causal dependencies between tasks such as sequential task specifications. We present our completed work that proposed LfD-STL framework that learns from even suboptimal/imperfect demonstrations and STL specifications to infer rewards for reinforcement learning tasks. We have validated our approach through various experimental setups to show how our method outperforms prior LfD methods. We then discuss future directions for tackling the problem of explainability and interpretability in such learning-based systems.

2021

1. 

   Learning From Demonstrations Using Signal Temporal Logic in Stochastic and Continuous Domains

   Aniruddh G. Puranic, Jyotirmoy V. Deshmukh, and Stefanos Nikolaidis

   IEEE Robotics and Automation Letters, 2021

   Bib HTML PDF

   @article{PDN21,
     author = {Puranic, Aniruddh G. and Deshmukh, Jyotirmoy V. and Nikolaidis, Stefanos},
     journal = {IEEE Robotics and Automation Letters},
     title = {Learning From Demonstrations Using Signal Temporal Logic in Stochastic and Continuous Domains},
     year = {2021},
     volume = {6},
     number = {4},
     pages = {6250-6257},
     doi = {10.1109/LRA.2021.3092676},
   }

2. 

   Learning from Demonstrations using Signal Temporal Logic

   Aniruddh Puranic, Jyotirmoy Deshmukh, and Stefanos Nikolaidis

   In Proceedings of the 2020 Conference on Robot Learning, 2021

   Abs Bib HTML PDF

   Learning-from-demonstrations is an emerging paradigm to obtain effective robot control policies for complex tasks via reinforcement learning without the need to explicitly design reward functions. However, it is susceptible to imperfections in demonstrations and also raises concerns of safety and interpretability in the learned control policies. To address these issues, we use Signal Temporal Logic to evaluate and rank the quality of demonstrations. Temporal logic-based specifications allow us to create non-Markovian rewards, and also define interesting causal dependencies between tasks such as sequential task specifications. We validate our approach through experiments on discrete-world and OpenAI Gym environments, and show that our approach outperforms the state-of-the-art Maximum Causal Entropy Inverse Reinforcement Learning.

   @inproceedings{PDN20,
     title = {Learning from Demonstrations using Signal Temporal Logic},
     author = {Puranic, Aniruddh and Deshmukh, Jyotirmoy and Nikolaidis, Stefanos},
     booktitle = {Proceedings of the 2020 Conference on Robot Learning},
     pages = {2228--2242},
     year = {2021},
     volume = {155},
     series = {Proceedings of Machine Learning Research},
     publisher = {PMLR},
   }

2020

1. HSCC

   Interpretable Classification of Time-Series Data Using Efficient Enumerative Techniques

   Sara Mohammadinejad, Jyotirmoy V. Deshmukh, Aniruddh G. Puranic, and 2 more authors

   In Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control, 2020

   Abs Bib

   Cyber-physical system applications such as autonomous vehicles, wearable devices, and avionic systems generate a large volume of time-series data. Designers often look for tools to help classify and categorize the data. Traditional machine learning techniques for time-series data offer several solutions to solve these problems; however, the artifacts trained by these algorithms often lack interpretability. On the other hand, temporal logic, such as Signal Temporal Logic (STL) have been successfully used in the formal methods community as specifications of time-series behaviors. In this work, we propose a new technique to automatically learn temporal logic formulas that are able to classify real-valued time-series data. Previous work on learning STL formulas from data either assumes a formula-template to be given by the user, or assumes some special fragment of STL that enables exploring the formula structure in a systematic fashion. In our technique, we relax these assumptions, and provide a way to systematically explore the space of all STL formulas. As the space of all STL formulas is very large, and contains many semantically equivalent formulas, we suggest a technique to heuristically prune the space of formulas considered. Finally, we illustrate our technique on various case studies from the automotive and transportation domains.

   @inproceedings{HSCC20,
     author = {Mohammadinejad, Sara and Deshmukh, Jyotirmoy V. and Puranic, Aniruddh G. and Vazquez-Chanlatte, Marcell and Donz\'{e}, Alexandre},
     title = {Interpretable Classification of Time-Series Data Using Efficient Enumerative Techniques},
     year = {2020},
     isbn = {9781450370189},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3365365.3382218},
     doi = {10.1145/3365365.3382218},
     booktitle = {Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control},
     articleno = {9},
     numpages = {10},
     keywords = {signal temporal logic, formal methods, machine learning, cyber-physical systems, time-series data, interpretability},
     location = {Sydney, New South Wales, Australia},
     series = {HSCC '20}
   }

2. ICCPS

   Mining Environment Assumptions for Cyber-Physical System Models

   Sara Mohammadinejad, Jyotirmoy V. Deshmukh, and Aniruddh G. Puranic

   In 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS), 2020

   Bib

   @inproceedings{ICCPS20,
     author = {Mohammadinejad, Sara and Deshmukh, Jyotirmoy V. and Puranic, Aniruddh G.},
     booktitle = {2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS)},
     title = {Mining Environment Assumptions for Cyber-Physical System Models},
     year = {2020},
     volume = {},
     number = {},
     pages = {87-97},
     doi = {10.1109/ICCPS48487.2020.00016}
   }

2019

1. MP35-04 AUTOMATED EVALUATION OF INSTRUMENT FORCE SENSITIVITY DURING ROBOTIC SUTURING UTILIZING VISION-BASED MACHINE LEARNING

   Aniruddh Puranic, Jian Chen*, Jessica Nguyen, and 2 more authors

   Journal of Urology, 2019
2. DATE

   Specifying and Evaluating Quality Metrics for Vision-based Perception Systems

   Anand Balakrishnan, Aniruddh G. Puranic, Xin Qin, and 4 more authors

   In 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2019

   Bib

   @inproceedings{DATE19,
     author = {Balakrishnan, Anand and Puranic, Aniruddh G. and Qin, Xin and Dokhanchi, Adel and Deshmukh, Jyotirmoy V. and Ben Amor, Heni and Fainekos, Georgios},
     booktitle = {2019 Design, Automation \& Test in Europe Conference \& Exhibition (DATE)},
     title = {Specifying and Evaluating Quality Metrics for Vision-based Perception Systems},
     year = {2019},
     volume = {},
     number = {},
     pages = {1433-1438},
     doi = {10.23919/DATE.2019.8715114}
   }

2016

1. Vehicle Number Plate Recognition System: A Literature Review and Implementation using Template Matching

   Aniruddh Gopinath Puranic, Kanwar Deepak, and V. Umadevi

   International Journal of Computer Applications, 2016