I am proud to say that our work presented at CoRL 2020 on robot learning-from-demonstrations using temporal logics was featured in many of the major Computer Science news platforms: The RISKS Digest (run by Peter G. Neumann of SRI International), ACM TechNews, USC News, USC Viterbi News and others.
The work on TQTL for vision-based perception systems was highlighted in USC Viterbi News.
- Learning from Demonstrations using Signal Temporal Logic
Aniruddh G. Puranic, Jyotirmoy V. Deshmukh, Stefanos Nikolaidis.
Conference on Robot Learning (CoRL), 2020.
- Interpretable Classification of Time-Series Data using Efficient Enumerative Techniques
Sara Mohammadinejad, Jyotirmoy V. Deshmukh, Aniruddh G. Puranic, Marcell Vazquez-Chanlatte, Alexandre Donzé.
23rd ACM International Conference on Hybrid Systems: Computation and Control (HSCC), 2020.
- Mining Environment Assumptions for Cyber-Physical System Models
Sara Mohammadinejad, Jyotirmoy V. Deshmukh, Aniruddh G. Puranic.
11th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), 2020.
- Poster: Automated Evaluation of Instrument Force Sensitivity During Robotic Suturing Utilizing Vision-Based Machine Learning
Aniruddh Puranic, Jian Chen, Jessica Nguyen, Jyotirmoy Deshmukh, Andrew Hung.
Journal of Urology, 2019.
- Specifying and Evaluating Quality Metrics for Vision-Based Perception Systems
Anand Balakrishnan, Aniruddh G. Puranic, Xin Qin, Adel Dokhanchi, Jyotirmoy V. Deshmukh, Heni Ben Amor, Georgios Fainekos.
IEEE Proceedings of Design, Automation and Test in Europe (DATE), 2019.
- Vehicle Number Plate Recognition System: A Literature Review and Implementation using Template Matching
Aniruddh Puranic, Deepak K. T., Umadevi V.
International Journal of Computer Applications (IJCA), 2016.