Artificial Intelligence

Making machines smarter for improved safety, efficiency and comfort.

Our AI research encompasses advances in computer vision, speech and audio processing, as well as data analytics. Key research themes include improved perception based on machine learning techniques, learning control policies through model-based reinforcement learning, as well as cognition and reasoning based on learned semantic representations. We apply our work to a broad range of automotive and robotics applications, as well as building and home systems.

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Researchers
Awards
- AWARD MERL team wins the Generative Data Augmentation of Room Acoustics (GenDARA) 2025 Challenge
  Date: April 7, 2025
  Awarded to: Christopher Ick, Gordon Wichern, Yoshiki Masuyama, François G. Germain, and Jonathan Le Roux
  MERL Contacts: Jonathan Le Roux; Yoshiki Masuyama; Gordon Wichern
  Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
  Brief
  - MERL's Speech & Audio team ranked 1st out of 3 teams in the Generative Data Augmentation of Room Acoustics (GenDARA) 2025 Challenge, which focused on “generating room impulse responses (RIRs) to supplement a small set of measured examples and using the augmented data to train speaker distance estimation (SDE) models". The team was led by MERL intern Christopher Ick, and also included Gordon Wichern, Yoshiki Masuyama, François G. Germain, and Jonathan Le Roux.
    
    The GenDARA Challenge was organized as part of the Generative Data Augmentation (GenDA) workshop at the 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2025), and held on April 7, 2025 in Hyderabad, India. Yoshiki Masuyama presented the team's method, "Data Augmentation Using Neural Acoustic Fields With Retrieval-Augmented Pre-training".
    
    The GenDARA challenge aims to promote the use of generative AI to synthesize RIRs from limited room data, as collecting or simulating RIR datasets at scale remains a significant challenge due to high costs and trade-offs between accuracy and computational efficiency. The challenge asked participants to first develop RIR generation systems capable of expanding a sparse set of labeled room impulse responses by generating RIRs at new source–receiver positions. They were then tasked with using this augmented dataset to train speaker distance estimation systems. Ranking was determined by the overall performance on the downstream SDE task. MERL’s approach to the GenDARA challenge centered on a geometry-aware neural acoustic field model that was first pre-trained on a large external RIR dataset to learn generalizable mappings from 3D room geometry to room impulse responses. For each challenge room, the model was then adapted or fine-tuned using the small number of provided RIRs, enabling high-fidelity generation of RIRs at unseen source–receiver locations. These augmented RIR sets were subsequently used to train the SDE system, improving speaker distance estimation by providing richer and more diverse acoustic training data.
- AWARD MERL Wins Awards at NeurIPS LLM Privacy Challenge
  Date: December 15, 2024
  Awarded to: Jing Liu, Ye Wang, Toshiaki Koike-Akino, Tsunato Nakai, Kento Oonishi, Takuya Higashi
  MERL Contacts: Toshiaki Koike-Akino; Jing Liu; Ye Wang
  Research Areas: Artificial Intelligence, Machine Learning, Information Security
  Brief
  - The Mitsubishi Electric Privacy Enhancing Technologies (MEL-PETs) team, consisting of a collaboration of MERL and Mitsubishi Electric researchers, won awards at the NeurIPS 2024 Large Language Model (LLM) Privacy Challenge. In the Blue Team track of the challenge, we won the 3rd Place Award, and in the Red Team track, we won the Special Award for Practical Attack.
- 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.
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News & Events
- NEWS MERL Researcher Diego Romeres Collaborates with Mitsubishi Electric and University of Padua to Advance Physics-Embedded AI for Predictive Equipment Maintenance
  Date: December 10, 2025
  MERL Contact: Diego Romeres
  Research Areas: Artificial Intelligence, Machine Learning, Robotics
  Brief
  - Mitsubishi Electric Research Laboratories (MERL) researchers, together with collaborators at Mitsubishi Electric’s Information Technology R&D Center in Kamakura, Kanagawa Prefecture, Japan, and the Department of Information Engineering at the University of Padua, have developed a cutting-edge physics-embedded AI technology that substantially improves the accuracy of equipment degradation estimation using minimal training data. This collaborative effort has culminated in a press release by Mitsubishi Electric Corporation announcing the new AI technology as part of its Neuro-Physical AI initiative under the Maisart program.
    
    The interdisciplinary team, including MERL Senior Principal Research Scientist and Team Leader Diego Romeres and University of Padua researchers Alberto Dalla Libera and Giulio Giacomuzzo, combined expertise in machine learning, physical modeling, and real-world industrial systems to embed physics-based models directly into AI frameworks. By training AI with theoretical physical laws and real operational data, the resulting system delivers reliable degradation estimates on the torque of robotic arms even with limited datasets. This result addresses key challenges in preventive maintenance for complex manufacturing environments and supports reduced downtime, maintained quality, and lower lifecycle costs.
    
    The successful integration of these foundational research efforts into Mitsubishi Electric’s business-scale AI solutions exemplifies MERL’s commitment to translating fundamental innovation into real-world impact.
- NEWS MERL Researchers at NeurIPS 2025 presented 2 conference papers, 5 workshop papers, and organized a workshop.
  Date: December 2, 2025 - December 7, 2025
  Where: San Diego
  MERL Contacts: Petros T. Boufounos; Anoop Cherian; Radu Corcodel; Stefano Di Cairano; Chiori Hori; Christopher R. Laughman; Suhas Anand Lohit; Pedro Miraldo; Saviz Mowlavi; Kuan-Chuan Peng; Arvind Raghunathan; Diego Romeres; Abraham P. Vinod; Pu (Perry) Wang
  Research Areas: Artificial Intelligence, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio
  Brief
  - MERL researchers presented 2 main-conference papers and 5 workshop papers, as well as organized a workshop, at NeurIPS 2025.
    
    Main Conference Papers:
    
    1) Sorachi Kato, Ryoma Yataka, Pu Wang, Pedro Miraldo, Takuya Fujihashi, and Petros Boufounos, "RAPTR: Radar-based 3D Pose Estimation using Transformer", Code available at: https://github.com/merlresearch/radar-pose-transformer
    
    2) Runyu Zhang, Arvind Raghunathan, Jeff Shamma, and Na Li, "Constrained Optimization From a Control Perspective via Feedback Linearization"
    
    Workshop Papers:
    
    1) Yuyou Zhang, Radu Corcodel, Chiori Hori, Anoop Cherian, and Ding Zhao, "SpinBench: Perspective and Rotation as a Lens on Spatial Reasoning in VLMs", NeuriIPS 2025 Workshop on SPACE in Vision, Language, and Embodied AI (SpaVLE) (Best Paper Runner-up)
    
    2) Xiaoyu Xie, Saviz Mowlavi, and Mouhacine Benosman, "Smooth and Sparse Latent Dynamics in Operator Learning with Jerk Regularization", Workshop on Machine Learning and the Physical Sciences (ML4PS)
    
    3) Spencer Hutchinson, Abraham Vinod, François Germain, Stefano Di Cairano, Christopher Laughman, and Ankush Chakrabarty, "Quantile-SMPC for Grid-Interactive Buildings with Multivariate Temporal Fusion Transformers", Workshop on UrbanAI: Harnessing Artificial Intelligence for Smart Cities (UrbanAI)
    
    4) Yuki Shirai, Kei Ota, Devesh Jha, and Diego Romeres, "Sim-to-Real Contact-Rich Pivoting via Optimization-Guided RL with Vision and Touch", Worskhop on Embodied World Models for Decision Making
    
    5) Mark Van der Merwe and Devesh Jha, "In-Context Policy Iteration for Dynamic Manipulation", Workshop on Embodied World Models for Decision Making
    
    Workshop Organized:
    
    MERL members co-organized the Multimodal Algorithmic Reasoning (MAR) Workshop (https://marworkshop.github.io/neurips25/). Organizers: Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Honglu Zhou (Salesforce AI Research), Kevin Smith (Massachusetts Institute of Technology), and Joshua B. Tenenbaum (Massachusetts Institute of Technology).
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Research Highlights
Internships
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Openings
- CI0177: Postdoctoral Research Fellow - Agentic AI
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Recent Publications
- Wilkinghoff, K., Yang, H., Ebbers, J., Germain, F.G., Wichern, G., Le Roux, J., "Local Density-Based Anomaly Score Normalization for Domain Generalization", IEEE Transactions on Audio, Speech and Language Processing, January 2026.
  BibTeX TR2026-010 PDF Software
  - @article{Wilkinghoff2026jan,
  - author = {Wilkinghoff, Kevin and Yang, Haici and Ebbers, Janek and Germain, François G and Wichern, Gordon and {Le Roux}, Jonathan},
  - title = {{Local Density-Based Anomaly Score Normalization for Domain Generalization}},
  - journal = {IEEE Transactions on Audio, Speech and Language Processing},
  - year = 2026,
  - month = jan,
  - url = {https://www.merl.com/publications/TR2026-010}
  - }
- Cornell, S., Boeddeker, C., Park, T., Huang, H., Raj, D., Wiesner, M., Masuyama, Y., Chang, X., Wang, Z.-Q., Squartini, S., Garcia, P., Watanabe, S., "Recent Trends in Distant Conversational Speech Recognition: A Review of CHiME-7 and 8 DASR Challenges", Computer Speech & Language, December 2025.
  BibTeX TR2026-008 PDF
  - @article{Cornell2025dec,
  - author = {Cornell, Samuele and Boeddeker, Christoph and Park, Taejin and Huang, He and Raj, Desh and Wiesner, Matthew and Masuyama, Yoshiki and Chang, Xuankai and Wang, Zhong-Qiu and Squartini, Stefano and Garcia, Paola and Watanabe, Shinji},
  - title = {{Recent Trends in Distant Conversational Speech Recognition: A Review of CHiME-7 and 8 DASR Challenges}},
  - journal = {Computer Speech \& Language},
  - year = 2025,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2026-008}
  - }
- Masuyama, Y., Wichern, G., Germain, F.G., Ick, C., Le Roux, J., "RANF: Neural Field-Based HRTF Spatial Upsampling with Retrieval Augmentation and Parameter Efficient Fine-Tuning", IEEE Open Journal of Signal Processing, December 2025.
  BibTeX TR2026-007 PDF Software
  - @article{Masuyama2025dec,
  - author = {Masuyama, Yoshiki and Wichern, Gordon and Germain, François G and Ick, Christopher and {Le Roux}, Jonathan},
  - title = {{RANF: Neural Field-Based HRTF Spatial Upsampling with Retrieval Augmentation and Parameter Efficient Fine-Tuning}},
  - journal = {IEEE Open Journal of Signal Processing},
  - year = 2025,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2026-007}
  - }
- Masuyama, Y., Wichern, G., Germain, F.G., Ick, C., Le Roux, J., "SuDaField: Subject- and Dataset-Aware Neural Field for HRTF Modeling", IEEE Open Journal of Signal Processing, December 2025.
  BibTeX TR2026-009 PDF
  - @article{Masuyama2025dec2,
  - author = {Masuyama, Yoshiki and Wichern, Gordon and Germain, François G and Ick, Christopher and {Le Roux}, Jonathan},
  - title = {{SuDaField: Subject- and Dataset-Aware Neural Field for HRTF Modeling}},
  - journal = {IEEE Open Journal of Signal Processing},
  - year = 2025,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2026-009}
  - }
- Hori, C., Masuyama, Y., Jain, S., Corcodel, R., Jha, D.K., Romeres, D., Le Roux, J., "Robot Confirmation Generation and Action Planning Using Long-context Q-Former Integrated with Multimodal LLM", IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), December 2025.
  BibTeX TR2025-167 PDF
  - @inproceedings{Hori2025dec,
  - author = {Hori, Chiori and Masuyama, Yoshiki and Jain, Siddarth and Corcodel, Radu and Jha, Devesh K. and Romeres, Diego and {Le Roux}, Jonathan},
  - title = {{Robot Confirmation Generation and Action Planning Using Long-context Q-Former Integrated with Multimodal LLM}},
  - booktitle = {IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)},
  - year = 2025,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2025-167}
  - }
- Van der Merwe, M., Jha, D.K., "In-Context Policy Iteration for Dynamic Manipulation", Advances in Neural Information Processing Systems (NeurIPS) Workshop on Embodied World Models for Decision Making, December 2025.
  BibTeX TR2025-163 PDF Video
  - @inproceedings{VanderMerwe2025dec,
  - author = {Van der Merwe, Mark and Jha, Devesh K.},
  - title = {{In-Context Policy Iteration for Dynamic Manipulation}},
  - booktitle = {Advances in Neural Information Processing Systems (NeurIPS) Workshop on Embodied World Models for Decision Making},
  - year = 2025,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2025-163}
  - }
- Xiang, X., Peng, K.-C., Lohit, S., Jones, M.J., Zhang, J., "Towards Open-Vocabulary Multimodal 3D Object Detection with Attributes", British Machine Vision Conference (BMVC), November 2025.
  BibTeX TR2025-162 PDF Video Data Presentation
  - @inproceedings{Xiang2025nov,
  - author = {{{Xiang, Xinhao and Peng, Kuan-Chuan and Lohit, Suhas and Jones, Michael J. and Zhang, Jiawei}}},
  - title = {{{Towards Open-Vocabulary Multimodal 3D Object Detection with Attributes}}},
  - booktitle = {British Machine Vision Conference (BMVC)},
  - year = 2025,
  - month = nov,
  - url = {https://www.merl.com/publications/TR2025-162}
  - }
- Masuyama, Y., "Neural Fields for Spatial Audio Modeling," Tech. Rep. TR2025-171, Speech and Audio in the Northeast (SANE), November 2025.
  BibTeX TR2025-171 PDF
  - @techreport{Masuyama2025nov,
  - author = {Masuyama, Yoshiki},
  - title = {{Neural Fields for Spatial Audio Modeling}},
  - institution = {Speech and Audio in the Northeast (SANE)},
  - year = 2025,
  - month = nov,
  - url = {https://www.merl.com/publications/TR2025-171}
  - }
See All Publications for Artificial Intelligence
Videos

[BMVC 2025] Towards Open-Vocabulary Multimodal 3D Object Detection with Attributes

[ICCV Workshop 2025] Joint Training of Image Generator and Detector for Road Defect Detection

[ICCV 2025] Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts

In-Context Iterative Policy Improvement for Dynamic Manipulation

[CVPR 2025] TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection

[MERL Seminar Series Spring 2025] Red Teaming AI Agents in-the-wild: Revealing Deployment Vulnerabilities

[MERL Seminar Series Spring 2025] The Emergence of Generalizability and Semantic Low-Dim Subspaces in Diffusion Models

[MERL Seminar Series Spring 2025] Amplifying human performance in combinatorial competitive programming

[WACV 2025] Towards Zero-shot 3D Anomaly Localization

[NeurIPS 2024] MEL-PETs Defense for the NeurIPS 2024 LLM Privacy Challenge Blue Team Track

[NeurIPS 2024] MEL-PETs Joint-Context Attack for the NeurIPS 2024 LLM Privacy Challenge Red Team Track

[MERL Seminar Series Fall 2024] AI-assisted Power Grid Dispatch and Control: Optimization, Safety, and Real-world Demonstrations

[NeurIPS 2024] Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads

[MERL Seminar Series Fall 2024] Audio for Object and Spatial Awareness

[IROS 2024] Few-shot Transparent Instance Segmentation for Bin Picking

[MERL Seminar Series Fall 2024] Tools from cognitive science to understand the behavior of large language models

[ECCV 2024] PS-NEUS: A Probability-guided Sampler for Neural Implicit Surface Rendering

[ECCV 2024] Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection

Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Sampling

[MERL Seminar Series Spring 2024] Are Emergent Abilities of Large Language Models a Mirage?

MERL's Quantum AI Technology

[MERL Seminar Series Spring 2024] The Debate Over 'Understanding' in AI's Large Language Models

[MERL Seminar Series Spring 2024] Computational models of human auditory and language processing

[MERL Seminar Series Fall 2023] Multiplicity in Machine Learning

Multi-level Reasoning for Robotic Assembly: From Sequence Inference to Contact Selection

[MERL Seminar Series Fall 2023] Visual Programming - A compositional approach to building General Purpose Vision Systems

[MERL Seminar Series Fall 2023] The Confluence of Vision, Language, and Robotics

Are Deep Neural Networks SMARTer than Second Graders?

[MERL Seminar Series Spring 2023] Fine-grained wildlife sound recognition: Towards the accuracy of a naturalist

[MERL Seminar Series Spring 2023] Pitfalls and Opportunities in Interpretable Machine Learning

Human Perspective Scene Understanding via Multimodal Sensing

[MERL Seminar Series Spring 2022] Self-Supervised Scene Representation Learning

[MERL Seminar Series Spring 2022] Learning Speech Representations with Multimodal Self-Supervision

[MERL Seminar Series 2021] Deep probabilistic regression

[MERL Seminar Series 2021] Learning to See by Moving: Self-supervising 3D scene representations for perception, control, and visual reasoning

[MERL Seminar Series 2021] Look and Listen: From Semantic to Spatial Audio-Visual Perception

Application of Deep Learning for Nanophotonic Device Design (Invited)

Machine Learning Power Amplifier

Scene-Aware Interaction Technology
Software & Data Downloads

Software & Data Downloads

MERL is making Artificial Intelligence software and data available to the research community:

MMHOI Dataset: Modeling Complex 3D Multi-Human Multi-Object Interactions (MMHOI)
Subject- and Dataset-Aware Neural Field for HRTF Modeling (SuDaField)
Open Vocabulary Attribute Detection Dataset (OVAD)
Task-Aware Unified Source Separation (TUSS)
Local Density-Based Anomaly Score Normalization for Domain Generalization (anomaly-score-normalization)
Long-Tailed Online Anomaly Detection dataset (LTOAD)

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