Diego Romeres

Diego Romeres
Diego's Quick Links

  • Biography

    Diego's research interests are in machine learning, system identification and robotic applications. At MERL he is currently working on applying nonparametric machine learning techniques for the control of robotic platforms. His Ph.D. thesis is about the combination of nonparametric data-driven models and physics-based models in gaussian processes for robot dynamics learning.

  • Recent News & Events

    •  NEWS    MERL Papers and Workshops at AAAI 2025
      Date: February 25, 2025 - March 4, 2025
      Where: The Association for the Advancement of Artificial Intelligence (AAAI)
      MERL Contacts: Ankush Chakrabarty; Toshiaki Koike-Akino; Jing Liu; Kuan-Chuan Peng; Diego Romeres; Ye Wang
      Research Areas: Artificial Intelligence, Machine Learning, Optimization
      Brief
      • MERL researchers presented 2 conference papers, 2 workshop papers, and co-organized 1 workshop at the AAAI 2025 conference, which was held in Philadelphia from Feb. 25 to Mar. 4, 2025. AAAI is one of the most prestigious and competitive international conferences in artificial intelligence (AI). Details of MERL contributions are provided below.

        - AAAI Papers in Main Tracks:

        1. "Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage" by M.R.U. Rashid, J. Liu, T. Koike-Akino, Y. Wang, and S. Mehnaz. [Oral Presentation]

        This work proposes a novel unlearning-based model poisoning method that amplifies privacy breaches during fine-tuning. Extensive empirical studies show the proposed method’s efficacy on both membership inference and data extraction attacks. The attack is stealthy enough to bypass detection based defenses, and differential privacy cannot effectively defend against the attacks without significantly impacting model utility.

        Paper: https://www.merl.com/publications/TR2025-017

        2. "User-Preference Meets Pareto-Optimality: Multi-Objective Bayesian Optimization with Local Gradient Search" by J.H.S. Ip, A. Chakrabarty, A. Mesbah, and D. Romeres. [Poster Presentation]

        This paper introduces a sample-efficient multi-objective Bayesian optimization method that integrates user preferences with gradient-based search to find near-Pareto optimal solutions. The proposed method achieves high utility and reduces distance to Pareto-front solutions across both synthetic and real-world problems, underscoring the importance of minimizing gradient uncertainty during gradient-based optimization. Additionally, the study introduces a novel utility function that respects Pareto dominance and effectively captures diverse user preferences.

        Paper: https://www.merl.com/publications/TR2025-018

        - AAAI Workshop Papers:

        1. "Quantum Diffusion Models for Few-Shot Learning" by R. Wang, Y. Wang, J. Liu, and T. Koike-Akino.

        This work presents the quantum diffusion model (QDM) as an approach to overcome the challenges of quantum few-shot learning (QFSL). It introduces three novel algorithms developed from complementary data-driven and algorithmic perspectives to enhance the performance of QFSL tasks. The extensive experiments demonstrate that these algorithms achieve significant performance gains over traditional baselines, underscoring the potential of QDM to advance QFSL by effectively leveraging quantum noise modeling and label guidance.

        Paper: https://www.merl.com/publications/TR2025-025

        2. "Quantum Implicit Neural Compression", by T. Fujihashi and T., Koike-Akino.

        This work introduces a quantum counterpart of implicit neural representation (quINR) which leverages the exponentially rich expressivity of quantum neural networks to improve the classical INR-based signal compression methods. Evaluations using some benchmark datasets show that the proposed quINR-based compression could improve rate-distortion performance in image compression compared with traditional codecs and classic INR-based coding methods.

        Paper: https://www.merl.com/publications/TR2025-024

        - AAAI Workshops Contributed by MERL:

        1. "Scalable and Efficient Artificial Intelligence Systems (SEAS)"

        K.-C. Peng co-organized this workshop, which offers a timely forum for experts to share their perspectives in designing and developing robust computer vision (CV), machine learning (ML), and artificial intelligence (AI) algorithms, and translating them into real-world solutions.

        Workshop link: https://seasworkshop.github.io/aaai25/index.html

        2. "Quantum Computing and Artificial Intelligence"

        T. Koike-Akino served a session chair of Quantum Neural Network in this workshop, which focuses on seeking contributions encompassing theoretical and applied advances in quantum AI, quantum computing (QC) to enhance classical AI, and classical AI to tackle various aspects of QC.

        Workshop link: https://sites.google.com/view/qcai2025/
    •  
    •  NEWS    MERL Researchers to Present 2 Conference and 11 Workshop Papers at NeurIPS 2024
      Date: December 10, 2024 - December 15, 2024
      Where: Advances in Neural Processing Systems (NeurIPS)
      MERL Contacts: Petros T. Boufounos; Matthew Brand; Ankush Chakrabarty; Anoop Cherian; François Germain; Toshiaki Koike-Akino; Christopher R. Laughman; Jonathan Le Roux; Jing Liu; Suhas Lohit; Tim K. Marks; Yoshiki Masuyama; Kieran Parsons; Kuan-Chuan Peng; Diego Romeres; Pu (Perry) Wang; Ye Wang; Gordon Wichern
      Research Areas: Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Human-Computer Interaction, Information Security
      Brief
      • MERL researchers will attend and present the following papers at the 2024 Advances in Neural Processing Systems (NeurIPS) Conference and Workshops.

        1. "RETR: Multi-View Radar Detection Transformer for Indoor Perception" by Ryoma Yataka (Mitsubishi Electric), Adriano Cardace (Bologna University), Perry Wang (Mitsubishi Electric Research Laboratories), Petros Boufounos (Mitsubishi Electric Research Laboratories), Ryuhei Takahashi (Mitsubishi Electric). Main Conference. https://neurips.cc/virtual/2024/poster/95530

        2. "Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads" by Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Joanna Matthiesen (Math Kangaroo USA), Kevin Smith (Massachusetts Institute of Technology), Josh Tenenbaum (Massachusetts Institute of Technology). Main Conference, Datasets and Benchmarks track. https://neurips.cc/virtual/2024/poster/97639

        3. "Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?" by Young-Jin Park (Massachusetts Institute of Technology), Jing Liu (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Gordon Wichern (Mitsubishi Electric Research Laboratories), Navid Azizan (Massachusetts Institute of Technology), Christopher R. Laughman (Mitsubishi Electric Research Laboratories), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories). Time Series in the Age of Large Models Workshop.

        4. "Forget to Flourish: Leveraging Model-Unlearning on Pretrained Language Models for Privacy Leakage" by Md Rafi Ur Rashid (Penn State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Shagufta Mehnaz (Penn State University), Ye Wang (Mitsubishi Electric Research Laboratories). Workshop on Red Teaming GenAI: What Can We Learn from Adversaries?

        5. "Spatially-Aware Losses for Enhanced Neural Acoustic Fields" by Christopher Ick (New York University), Gordon Wichern (Mitsubishi Electric Research Laboratories), Yoshiki Masuyama (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Jonathan Le Roux (Mitsubishi Electric Research Laboratories). Audio Imagination Workshop.

        6. "FV-NeRV: Neural Compression for Free Viewpoint Videos" by Sorachi Kato (Osaka University), Takuya Fujihashi (Osaka University), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Takashi Watanabe (Osaka University). Machine Learning and Compression Workshop.

        7. "GPT Sonography: Hand Gesture Decoding from Forearm Ultrasound Images via VLM" by Keshav Bimbraw (Worcester Polytechnic Institute), Ye Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). AIM-FM: Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond Workshop.

        8. "Smoothed Embeddings for Robust Language Models" by Hase Ryo (Mitsubishi Electric), Md Rafi Ur Rashid (Penn State University), Ashley Lewis (Ohio State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kieran Parsons (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories). Safe Generative AI Workshop.

        9. "Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation" by Xiangyu Chen (University of Kansas), Ye Wang (Mitsubishi Electric Research Laboratories), Matthew Brand (Mitsubishi Electric Research Laboratories), Pu Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). Workshop on Adaptive Foundation Models.

        10. "Preference-based Multi-Objective Bayesian Optimization with Gradients" by Joshua Hang Sai Ip (University of California Berkeley), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Ali Mesbah (University of California Berkeley), Diego Romeres (Mitsubishi Electric Research Laboratories). Workshop on Bayesian Decision-Making and Uncertainty. Lightning talk spotlight.

        11. "TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensions with Trust-Region-based Bayesian Novelty Search" by Wei-Ting Tang (Ohio State University), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Joel A. Paulson (Ohio State University). Workshop on Bayesian Decision-Making and Uncertainty.

        12. "MEL-PETs Joint-Context Attack for the NeurIPS 2024 LLM Privacy Challenge Red Team Track" by Ye Wang (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Special Award for Practical Attack.

        13. "MEL-PETs Defense for the NeurIPS 2024 LLM Privacy Challenge Blue Team Track" by Jing Liu (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Won 3rd Place Award.

        MERL members also contributed to the organization of the Multimodal Algorithmic Reasoning (MAR) Workshop (https://marworkshop.github.io/neurips24/). Organizers: Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Honglu Zhou (Salesforce Research), Kevin Smith (Massachusetts Institute of Technology), Tim K. Marks (Mitsubishi Electric Research Laboratories), Juan Carlos Niebles (Salesforce AI Research), Petar Veličković (Google DeepMind).
    •  

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  • Awards

    •  AWARD    University of Padua and MERL team wins the AI Olympics with RealAIGym competition at IROS24
      Date: October 17, 2024
      Awarded to: Niccolò Turcato, Alberto Dalla Libera, Giulio Giacomuzzo, Ruggero Carli, Diego Romeres
      MERL Contact: Diego Romeres
      Research Areas: Artificial Intelligence, Dynamical Systems, Machine Learning, Robotics
      Brief
      • The team composed of the control group at the University of Padua and MERL's Optimization and Robotic team ranked 1st out of the 4 finalist teams that arrived to the 2nd AI Olympics with RealAIGym competition at IROS 24, which focused on control of under-actuated robots. The team was composed by Niccolò Turcato, Alberto Dalla Libera, Giulio Giacomuzzo, Ruggero Carli and Diego Romeres. The competition was organized by the German Research Center for Artificial Intelligence (DFKI), Technical University of Darmstadt and Chalmers University of Technology.

        The competition and award ceremony was hosted by IEEE International Conference on Intelligent Robots and Systems (IROS) on October 17, 2024 in Abu Dhabi, UAE. Diego Romeres presented the team's method, based on a model-based reinforcement learning algorithm called MC-PILCO.
    •  
    •  AWARD    Honorable Mention Award at NeurIPS 23 Instruction Workshop
      Date: December 15, 2023
      Awarded to: Lingfeng Sun, Devesh K. Jha, Chiori Hori, Siddharth Jain, Radu Corcodel, Xinghao Zhu, Masayoshi Tomizuka and Diego Romeres
      MERL Contacts: Radu Corcodel; Chiori Hori; Siddarth Jain; Devesh K. Jha; Diego Romeres
      Research Areas: Artificial Intelligence, Machine Learning, Robotics
      Brief
      • MERL Researchers received an "Honorable Mention award" at the Workshop on Instruction Tuning and Instruction Following at the NeurIPS 2023 conference in New Orleans. The workshop was on the topic of instruction tuning and Instruction following for Large Language Models (LLMs). MERL researchers presented their work on interactive planning using LLMs for partially observable robotic tasks during the oral presentation session at the workshop.
    •  
    •  AWARD    Joint University of Padua-MERL team wins Challenge 'AI Olympics With RealAIGym'
      Date: August 25, 2023
      Awarded to: Alberto Dalla Libera, Niccolo' Turcato, Giulio Giacomuzzo, Ruggero Carli, Diego Romeres
      MERL Contact: Diego Romeres
      Research Areas: Artificial Intelligence, Machine Learning, Robotics
      Brief
      • A joint team consisting of members of University of Padua and MERL ranked 1st in the IJCAI2023 Challenge "Al Olympics With RealAlGym: Is Al Ready for Athletic Intelligence in the Real World?". The team was composed by MERL researcher Diego Romeres and a team from University Padua (UniPD) consisting of Alberto Dalla Libera, Ph.D., Ph.D. Candidates: Niccolò Turcato, Giulio Giacomuzzo and Prof. Ruggero Carli from University of Padua.

        The International Joint Conference on Artificial Intelligence (IJCAI) is a premier gathering for AI researchers and organizes several competitions. This year the competition CC7 "AI Olympics With RealAIGym: Is AI Ready for Athletic Intelligence in the Real World?" consisted of two stages: simulation and real-robot experiments on two under-actuated robotic systems. The two robotics systems were treated as separate tracks and one final winner was selected for each track based on specific performance criteria in the control tasks.

        The UniPD-MERL team competed and won in both tracks. The team's system made strong use of a Model-based Reinforcement Learning algorithm called (MC-PILCO) that we recently published in the journal IEEE Transaction on Robotics.
    •  
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  • MERL Publications

    •  Hori, C., Kambara, M., Sugiura, K., Ota, K., Khurana, S., Jain, S., Corcodel, R., Jha, D.K., Romeres, D., Le Roux, J., "Interactive Robot Action Replanning using Multimodal LLM Trained from Human Demonstration Videos", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2025.
      BibTeX TR2025-034 PDF
      • @inproceedings{Hori2025mar,
      • author = {Hori, Chiori and Kambara, Motonari and Sugiura, Komei and Ota, Kei and Khurana, Sameer and Jain, Siddarth and Corcodel, Radu and Jha, Devesh K. and Romeres, Diego and {Le Roux}, Jonathan},
      • title = {{Interactive Robot Action Replanning using Multimodal LLM Trained from Human Demonstration Videos}},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2025,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2025-034}
      • }
    •  Shao, K., Chakrabarty, A., Mesbah, A., Romeres, D., "Coactive Preference-Guided Multi-Objective Bayesian Optimization: An Application to Policy Learning in Personalized Plasma Medicine", IEEE Control Systems Letters, February 2025.
      BibTeX TR2025-021 PDF
      • @article{Shao2025feb,
      • author = {Shao, Ketong and Chakrabarty, Ankush and Mesbah, Ali and Romeres, Diego},
      • title = {{Coactive Preference-Guided Multi-Objective Bayesian Optimization: An Application to Policy Learning in Personalized Plasma Medicine}},
      • journal = {IEEE Control Systems Letters},
      • year = 2025,
      • month = feb,
      • url = {https://www.merl.com/publications/TR2025-021}
      • }
    •  Ip, J.H.S., Chakrabarty, A., Mesbah, A., Romeres, D., "User Preference Meets Pareto-Optimality in Multi-Objective Bayesian Optimization", AAAI Conference on Artificial Intelligence, February 2025.
      BibTeX TR2025-018 PDF
      • @inproceedings{Ip2025feb,
      • author = {{{Ip, Joshua Hang Sai and Chakrabarty, Ankush and Mesbah, Ali and Romeres, Diego}}},
      • title = {{{User Preference Meets Pareto-Optimality in Multi-Objective Bayesian Optimization}}},
      • booktitle = {AAAI Conference on Artificial Intelligence},
      • year = 2025,
      • month = feb,
      • url = {https://www.merl.com/publications/TR2025-018}
      • }
    •  Chen, Y., Jha, D.K., Tomizuka, M., Romeres, D., "FDPP: Fine-tune Diffusion Policy with Human Preference", arXiv, January 2025.
      BibTeX arXiv
      • @article{Chen2025jan,
      • author = {Chen, Yuxin and Jha, Devesh K. and Tomizuka, Masayoshi and Romeres, Diego},
      • title = {{FDPP: Fine-tune Diffusion Policy with Human Preference}},
      • journal = {arXiv},
      • year = 2025,
      • month = jan,
      • url = {https://arxiv.org/abs/2501.08259}
      • }
    •  Shirai, Y., Jha, D.K., Raghunathan, A., Romeres, D., "Chance-Constrained Optimization for Contact-rich Systems using Mixed Integer Programming", Nonlinear Analysis: Hybrid Systems, DOI: 10.1016/​j.nahs.2024.101466, Vol. 52, December 2024.
      BibTeX TR2024-008 PDF
      • @article{Shirai2024dec,
      • author = {Shirai, Yuki and Jha, Devesh K. and Raghunathan, Arvind and Romeres, Diego},
      • title = {{Chance-Constrained Optimization for Contact-rich Systems using Mixed Integer Programming}},
      • journal = {Nonlinear Analysis: Hybrid Systems},
      • year = 2024,
      • volume = 52,
      • month = dec,
      • doi = {10.1016/j.nahs.2024.101466},
      • issn = {1751-570X},
      • url = {https://www.merl.com/publications/TR2024-008}
      • }
    See All MERL Publications for Diego

  • Other Publications

    •  Diego Romeres, Giulia Prando, Gianluigi Pillonetto and Alessandro Chiuso, "On-line bayesian system identification", Control Conference (ECC), 2016 European, 2016, pp. 1359-1364.
      BibTeX
      • @Inproceedings{romeres2016line,
      • author = {Romeres, Diego and Prando, Giulia and Pillonetto, Gianluigi and Chiuso, Alessandro},
      • title = {On-line bayesian system identification},
      • booktitle = {Control Conference (ECC), 2016 European},
      • year = 2016,
      • pages = {1359--1364},
      • organization = {IEEE}
      • }
    •  Diego Romeres, Mattia Zorzi, Raffaello Camoriano and Alessandro Chiuso, "Online semi-parametric learning for inverse dynamics modeling", Decision and Control (CDC), 2016 IEEE 55th Conference on, 2016, pp. 2945-2950.
      BibTeX
      • @Inproceedings{romeres2016online,
      • author = {Romeres, Diego and Zorzi, Mattia and Camoriano, Raffaello and Chiuso, Alessandro},
      • title = {Online semi-parametric learning for inverse dynamics modeling},
      • booktitle = {Decision and Control (CDC), 2016 IEEE 55th Conference on},
      • year = 2016,
      • pages = {2945--2950},
      • organization = {IEEE}
      • }
    •  Diego Romeres, Mattia Zorzi, Raffaello Camoriano and Alessandro Chiuso, "Online semi-parametric learning for inverse dynamics modeling", Decision and Control (CDC), 2016 IEEE 55th Conference on, 2016, pp. 2945-2950.
      BibTeX
      • @Inproceedings{romeres2016onlinesemiparametric,
      • author = {Romeres, Diego and Zorzi, Mattia and Camoriano, Raffaello and Chiuso, Alessandro},
      • title = {Online semi-parametric learning for inverse dynamics modeling},
      • booktitle = {Decision and Control (CDC), 2016 IEEE 55th Conference on},
      • year = 2016,
      • pages = {2945--2950},
      • organization = {IEEE}
      • }
    •  Giulia Prando, Diego Romeres, Gianluigi Pillonetto and Alessandro Chiuso, "Classical vs. Bayesian methods for linear system identification: Point estimators and confidence sets", Control Conference (ECC), 2016 European, 2016, pp. 1365-1370.
      BibTeX
      • @Inproceedings{tprando2016classical,
      • author = {Prando, Giulia and Romeres, Diego and Pillonetto, Gianluigi and Chiuso, Alessandro},
      • title = {Classical vs. Bayesian methods for linear system identification: Point estimators and confidence sets},
      • booktitle = {Control Conference (ECC), 2016 European},
      • year = 2016,
      • pages = {1365--1370},
      • organization = {IEEE}
      • }
    •  Giulia Prando, Diego Romeres and Alessandro Chiuso, "Online identification of time-varying systems: A Bayesian approach", Decision and Control (CDC), 2016 IEEE 55th Conference on, 2016, pp. 3775-3780.
      BibTeX
      • @Inproceedings{tprando2016online,
      • author = {Prando, Giulia and Romeres, Diego and Chiuso, Alessandro},
      • title = {Online identification of time-varying systems: A Bayesian approach},
      • booktitle = {Decision and Control (CDC), 2016 IEEE 55th Conference on},
      • year = 2016,
      • pages = {3775--3780},
      • organization = {IEEE}
      • }
    •  Ulrich Muenz and Diego Romeres, "Region of attraction of power systems", IFAC Proceedings Volumes, Vol. 46, No. 27, pp. 49-54, 2013.
      BibTeX
      • @Article{munz2013region,
      • author = {Muenz, Ulrich and Romeres, Diego},
      • title = {Region of attraction of power systems},
      • journal = {IFAC Proceedings Volumes},
      • year = 2013,
      • volume = 46,
      • number = 27,
      • pages = {49--54},
      • publisher = {Elsevier}
      • }
    •  Diego Romeres, Florian Doerfler and Francesco Bullo, "Novel results on slow coherency in consensus and power networks", Control Conference (ECC), 2013 European, 2013, pp. 742-747.
      BibTeX
      • @Inproceedings{romeres2013novel,
      • author = {Romeres, Diego and Doerfler, Florian and Bullo, Francesco},
      • title = {Novel results on slow coherency in consensus and power networks},
      • booktitle = {Control Conference (ECC), 2013 European},
      • year = 2013,
      • pages = {742--747},
      • organization = {IEEE}
      • }
    •  Saverio Bolognani, Andrea Carron, Alberto Di Vittorio, Diego Romeres, Luca Schenato and Sandro Zampieri, "Distributed multi-hop reactive power compensation in smart micro-grids subject to saturation constraints", Decision and Control (CDC), 2012 IEEE 51st Annual Conference on, 2012, pp. 1118-1123.
      BibTeX
      • @Inproceedings{bolognani2012distributed,
      • author = {Bolognani, Saverio and Carron, Andrea and Di Vittorio, Alberto and Romeres, Diego and Schenato, Luca and Zampieri, Sandro},
      • title = {Distributed multi-hop reactive power compensation in smart micro-grids subject to saturation constraints},
      • booktitle = {Decision and Control (CDC), 2012 IEEE 51st Annual Conference on},
      • year = 2012,
      • pages = {1118--1123},
      • organization = {IEEE}
      • }

  • Software & Data Downloads

  • Videos

  • MERL Issued Patents

    • Title: "System and Method for Robust Robotic Manipulation using Chance Constrained Optimization"
      Inventors: Jha, Devesh; Raghunathan, Arvind U.; Romeres, Diego
      Patent No.: 12,049,007
      Issue Date: Jul 30, 2024
    • Title: "OBJECT MANIPULATION WITH COLLISION AVOIDANCE USING COMPLEMENTARITY CONSTRAINTS"
      Inventors: Raghunathan, Arvind U.; Jha, Devesh; Romeres, Diego
      Patent No.: 11,883,962
      Issue Date: Jan 30, 2024
    • Title: "System and Method for Robotic Assembly Based on Adaptive Compliance"
      Inventors: Nikovski, Daniel N.; Romeres, Diego; Jha, Devesh; Yerazunis, William S.
      Patent No.: 11,673,264
      Issue Date: Jun 13, 2023
    • Title: "System and Method for Policy Optimization using Quasi-Newton Trust Region Method"
      Inventors: Jha, Devesh; Raghunathan, Arvind U; Romeres, Diego
      Patent No.: 11,650,551
      Issue Date: May 16, 2023
    • Title: "Systems and Methods Automatic Anomaly Detection in Mixed Human-Robot Manufacturing Processes"
      Inventors: Laftchiev, Emil; Romeres, Diego
      Patent No.: 11,472,028
      Issue Date: Oct 18, 2022
    • Title: "Systems and Methods for Advance Anomaly Detection in a Discrete Manufacturing Process with a Task Performed by a Human-Robot Team"
      Inventors: Laftchiev, Emil; Romeres, Diego
      Patent No.: 11,442,429
      Issue Date: Sep 13, 2022
    • Title: "System and Design of Derivative-free Model Learning for Robotic Systems"
      Inventors: Romeres, Diego; Libera, Alberto Dalla; Jha, Devesh; Nikovski, Daniel Nikolaev
      Patent No.: 11,389,957
      Issue Date: Jul 19, 2022
    • Title: "System and Method for Thermal Control Based on Invertible Causation Relationship"
      Inventors: Laftchiev, Emil; Nikovski, Daniel N.; Romeres, Diego
      Patent No.: 11,280,514
      Issue Date: Mar 22, 2022
    • Title: "System and Method for Automatic Error Recovery in Robotic Assembly"
      Inventors: Nikovski, Daniel Nikolaev; Jha, Devesh; Romeres, Diego
      Patent No.: 11,161,244
      Issue Date: Nov 2, 2021
    See All Patents for MERL