publications

2025

1. arXiv

   

   RoboTwin: A Robotic Teleoperation Framework Using Digital Twins

   Harsha Yelchuri, Diwakar Kumar Singh, Nithish Krishnabharathi Gnani, and 2 more authors

   2025

   arXiv Bib Video Website

   @misc{yelchuri2025robotwin,
     title = {RoboTwin: A Robotic Teleoperation Framework Using Digital Twins},
     author = {Yelchuri, Harsha and Singh, Diwakar Kumar and Gnani, Nithish Krishnabharathi and Prabhakar, T. V. and Singh, Chandramani},
     year = {2025},
     eprint = {2506.01027},
     archiveprefix = {arXiv},
     primaryclass = {cs.RO},
     doi = {10.48550/arXiv.2506.01027},
     url = {https://arxiv.org/abs/2506.01027},
   }

2024

1. NCC

   

   Towards a Resource-Efficient Semi-Asynchronous Federated Learning for Heterogeneous Devices

   Zitha Sasindran, Harsha Yelchuri, and T. V. Prabhakar

   In 2024 National Conference on Communications (NCC), 2024
2. Interspeech

   

   SeMaScore : a new evaluation metric for automatic speech recognition tasks

   Zitha Sasindran, Harsha Yelchuri, and T. V. Prabhakar

   2024
3. COMSNETS

   

   SmartSV: Real-Time Solenoid Valve Diagnosis at Embedded Edge-Level

   Harsha Yelchuri, Shrutkirthi Godkhindi, Prajwal BN, and 2 more authors

   In 2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS), 2024
4. COMSNETS

   

   GASP: Gap Analysis for Spark Plug

   Harsha Yelchuri, Abhigna V Kumar, Siddharth Sahu, and 3 more authors

   In 2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS), 2024
5. AIMLSystems

   

   MobileASR: A resource-aware on-device learning framework for user voice personalization applications on mobile phones

   Zitha Sasindran, Harsha Yelchuri, Prabhakar Venkata Tamma, and 1 more author

   In Proceedings of the Third International Conference on AI-ML Systems, Bangalore, India, 2024

   Abs

   We describe a comprehensive methodology for developing user-voice personalized automatic speech recognition (ASR) models by effectively training models on mobile phones, allowing user data and models to be stored and used locally. To achieve this, we propose a resource-aware sub-model-based training approach that considers the RAM, and battery capabilities of mobile phones. By considering the evaluation metric and resource constraints of the mobile phones, we are able to perform efficient training and halt the process accordingly. To simulate real users, we use speakers with various accents. The entire on-device training and evaluation framework was then tested on various mobile phones across brands. We show that fine-tuning the models and selecting the right hyperparameter values is a trade-off between the lowest achievable performance metric, on-device training time, and memory consumption. Overall, our methodology offers a comprehensive solution for developing personalized ASR models while leveraging the capabilities of mobile phones, and balancing the need for accuracy with resource constraints.

2023

1. ASRU

   

   HEVAL: A New Hybrid Evaluation Metric for Automatic Speech Recognition Tasks

   Zitha Sasindran, Harsha Yelchuri, T. V. Prabhakar, and 1 more author

   In 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2023
2. IJCNN

   

   Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devices

   Zitha Sasindran, Harsha Yelchuri, and T. V. Prabhakar

   In 2023 International Joint Conference on Neural Networks (IJCNN), 2023

2022

1. arXiv

   

   A review of TinyML

   Harsha Yelchuri, and Rashmi R

   2022
2. arXiv

   

   PreMa: Predictive Maintenance of Solenoid Valve in Real-Time at Embedded Edge-Level

   Prajwal BN, Harsha Yelchuri, Vishwanath Shastry, and 1 more author

   2022