Artificial Intelligence

Researchers

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 MERL team wins the Listener Acoustic Personalisation (LAP) 2024 Challenge
Date: August 29, 2024
Awarded to: Yoshiki Masuyama, Gordon Wichern, Francois G. Germain, Christopher Ick, and Jonathan Le Roux
MERL Contacts: François Germain; Jonathan Le Roux; Gordon Wichern; Yoshiki Masuyama
Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
Brief
- MERL's Speech & Audio team ranked 1st out of 7 teams in Task 2 of the 1st SONICOM Listener Acoustic Personalisation (LAP) Challenge, which focused on "Spatial upsampling for obtaining a high-spatial-resolution HRTF from a very low number of directions". The team was led by Yoshiki Masuyama, and also included Gordon Wichern, Francois Germain, MERL intern Christopher Ick, and Jonathan Le Roux.
  
  The LAP Challenge workshop and award ceremony was hosted by the 32nd European Signal Processing Conference (EUSIPCO 24) on August 29, 2024 in Lyon, France. Yoshiki Masuyama presented the team's method, "Retrieval-Augmented Neural Field for HRTF Upsampling and Personalization", and received the award from Prof. Michele Geronazzo (University of Padova, IT, and Imperial College London, UK), Chair of the Challenge's Organizing Committee.
  
  The LAP challenge aims to explore challenges in the field of personalized spatial audio, with the first edition focusing on the spatial upsampling and interpolation of head-related transfer functions (HRTFs). HRTFs with dense spatial grids are required for immersive audio experiences, but their recording is time-consuming. Although HRTF spatial upsampling has recently shown remarkable progress with approaches involving neural fields, HRTF estimation accuracy remains limited when upsampling from only a few measured directions, e.g., 3 or 5 measurements. The MERL team tackled this problem by proposing a retrieval-augmented neural field (RANF). RANF retrieves a subject whose HRTFs are close to those of the target subject at the measured directions from a library of subjects. The HRTF of the retrieved subject at the target direction is fed into the neural field in addition to the desired sound source direction. The team also developed a neural network architecture that can handle an arbitrary number of retrieved subjects, inspired by a multi-channel processing technique called transform-average-concatenate.
AWARD Jonathan Le Roux elevated to IEEE Fellow
Date: January 1, 2024
Awarded to: Jonathan Le Roux
MERL Contact: Jonathan Le Roux
Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
Brief
- MERL Distinguished Scientist and Speech & Audio Senior Team Leader Jonathan Le Roux has been elevated to IEEE Fellow, effective January 2024, "for contributions to multi-source speech and audio processing."
  
  Mitsubishi Electric celebrated Dr. Le Roux's elevation and that of another researcher from the company, Dr. Shumpei Kameyama, with a worldwide news release on February 15.
  
  Dr. Jonathan Le Roux has made fundamental contributions to the field of multi-speaker speech processing, especially to the areas of speech separation and multi-speaker end-to-end automatic speech recognition (ASR). His contributions constituted a major advance in realizing a practically usable solution to the cocktail party problem, enabling machines to replicate humans’ ability to concentrate on a specific sound source, such as a certain speaker within a complex acoustic scene—a long-standing challenge in the speech signal processing community. Additionally, he has made key contributions to the measures used for training and evaluating audio source separation methods, developing several new objective functions to improve the training of deep neural networks for speech enhancement, and analyzing the impact of metrics used to evaluate the signal reconstruction quality. Dr. Le Roux’s technical contributions have been crucial in promoting the widespread adoption of multi-speaker separation and end-to-end ASR technologies across various applications, including smart speakers, teleconferencing systems, hearables, and mobile devices.
  
  IEEE Fellow is the highest grade of membership of the IEEE. It honors members with an outstanding record of technical achievements, contributing importantly to the advancement or application of engineering, science and technology, and bringing significant value to society. Each year, following a rigorous evaluation procedure, the IEEE Fellow Committee recommends a select group of recipients for elevation to IEEE Fellow. Less than 0.1% of voting members are selected annually for this member grade elevation.

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News & Events

TALK [MERL Seminar Series 2024] Samuel Clarke presents talk titled Audio for Object and Spatial Awareness
Date & Time: Wednesday, October 30, 2024; 1:00 PM
Speaker: Samuel Clarke, Stanford University
MERL Host: Gordon Wichern
Research Areas: Artificial Intelligence, Machine Learning, Robotics, Speech & Audio
Abstract
- Acoustic perception is invaluable to humans and robots in understanding objects and events in their environments. These sounds are dependent on properties of the source, the environment, and the receiver. Many humans possess remarkable intuition both to infer key properties of each of these three aspects from a sound and to form expectations of how these different aspects would affect the sound they hear. In order to equip robots and AI agents with similar if not stronger capabilities, our research has taken a two-fold path. First, we collect high-fidelity datasets in both controlled and uncontrolled environments which capture real sounds of objects and rooms. Second, we introduce differentiable physics-based models that can estimate acoustic properties of objects and rooms from minimal amounts of real audio data, then can predict new sounds from these objects and rooms under novel, “unseen” conditions.
TALK [MERL Seminar Series 2024] Zhaojian Li presents talk titled A Multi-Arm Robotic System for Robotic Apple Harvesting
Date & Time: Wednesday, October 2, 2024; 1:00 PM
Speaker: Zhaojian Li, Mivchigan State University
MERL Host: Yebin Wang
Research Areas: Artificial Intelligence, Computer Vision, Control, Robotics
Abstract
- Harvesting labor is the single largest cost in apple production in the U.S. Surging cost and growing shortage of labor has forced the apple industry to seek automated harvesting solutions. Despite considerable progress in recent years, the existing robotic harvesting systems still fall short of performance expectations, lacking robustness and proving inefficient or overly complex for practical commercial deployment. In this talk, I will present the development and evaluation of a new dual-arm robotic apple harvesting system. This work is a result of a continuous collaboration between Michigan State University and U.S. Department of Agriculture.

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Research Highlights

Internships

CV0050: Internship - Anomaly Localization for Industrial Inspection

MERL is looking for a self-motivated intern to work on anomaly localization in industrial inspection setting using computer vision. The relevant topics in the scope include (but not limited to): cross-view image anomaly localization, how to train one model for multiple views and defect types, how to incorporate large foundation models in image anomaly localization, etc. The candidates with experiences of image anomaly localization in industrial inspection settings (e.g., MVTec-AD or VisA datasets) and usage of large foundation models are strongly preferred. The ideal candidate would be a PhD student with a strong background in computer vision and machine learning, and the candidate is expected to have published at least one paper in a top-tier computer vision, machine learning, or artificial intelligence venues, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, or AAAI. Proficiency in Python programming and familiarity in at least one deep learning framework are necessary. The ideal candidate is required to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The duration of the internship is ideally to be at least 3 months with a flexible start date.
Required Specific Experience
- Experience with Python, PyTorch, and large foundation models (e.g. CLIP, ALIGN, etc.).
CI0066: Internship - IoT Network Anomaly Detection

MERL is seeking a highly motivated and qualified intern to conduct research on IoT network anomaly detection and analysis. The candidate is expected to develop innovative anomaly detection technologies that can proactively detect and analyze network failure in large-scale IOT networks. The candidate should have knowledge of LLM/ML and anomaly detection. Knowledge of network log analysis and network protocol a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. Start date for this internship is flexible and the duration is 3 months.
The responsibilities of this intern position include (i) research on anomaly detection in large-scale IoT networks; (ii) develop proactive network anomaly detection and analysis technologies; (iii) simulate and analyze the performance of developed technology.
CV0051: Internship - Visual-LiDAR fused object detection and recognition

MERL is looking for a self-motivated intern to work on visual-LiDAR fused object detection and recognition using computer vision. The relevant topics in the scope include (but not limited to): open-vocabulary visual-LiDAR object detection and recognition, domain adaptation or generalization in visual-LiDAR object detection, data-efficient methods for visual-LiDAR object detection, small object detection with visual-LiDAR input, etc. The candidates with experiences of object recognition in LiDAR are strongly preferred. The ideal candidate would be a PhD student with a strong background in computer vision and machine learning, and the candidate is expected to have published at least one paper in a top-tier computer vision, machine learning, or artificial intelligence venues, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, or AAAI. Proficiency in Python programming and familiarity in at least one deep learning framework are necessary. The ideal candidate is required to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The duration of the internship is ideally to be at least 3 months with a flexible start date.
Required Specific Experience
- Experience with Python, PyTorch, and datasets with both images and LiDAR (e.g. the nuScenes dataset).

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Recent Publications

Chen, X., Wang, Y., Brand, M., Wang, P., Liu, J., Koike-Akino, T., "Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation", Advances in Neural Information Processing Systems (NeurIPS), December 2024.
BibTeX TR2024-157 PDF
- @inproceedings{Chen2024dec,
- author = {Chen, Xiangyu and Wang, Ye and Brand, Matthew and Wang, Pu and Liu, Jing and Koike-Akino, Toshiaki}},
- title = {Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation},
- booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
- year = 2024,
- month = dec,
- url = {https://www.merl.com/publications/TR2024-157}
- }
Chen, X., Liu, J., Wang, Y., Wang, P., Brand, M., Wang, G., Koike-Akino, T., "SuperLoRA: Parameter-Efficient Unified Adaptation of Large Foundation Models", British Machine Vision Conference (BMVC), November 2024.
BibTeX TR2024-156 PDF
- @inproceedings{Chen2024nov,
- author = {Chen, Xiangyu and Liu, Jing and Wang, Ye and Wang, Pu and Brand, Matthew and Wang, Guanghui and Koike-Akino, Toshiaki}},
- title = {SuperLoRA: Parameter-Efficient Unified Adaptation of Large Foundation Models},
- booktitle = {British Machine Vision Conference (BMVC)},
- year = 2024,
- month = nov,
- url = {https://www.merl.com/publications/TR2024-156}
- }
Cornell, S., Ebbers, J., Douwes, C., Martin-Morato, I., Harju, M., Mesaros, A., Serizel, R., "DCASE 2024 Task 4: Sound Event Detection with Heterogeneous Data and Missing Labels", Detection and Classification of Acoustic Scenes and Events (DCASE) Workshop, October 2024.
BibTeX TR2024-146 PDF
- @inproceedings{Cornell2024oct,
- author = {Cornell, Samuele and Ebbers, Janek and Douwes, Constance and Martin-Morato, Irene and Harju, Manu and Mesaros, Annamaria and Serizel, Romain}},
- title = {DCASE 2024 Task 4: Sound Event Detection with Heterogeneous Data and Missing Labels},
- booktitle = {Detection and Classification of Acoustic Scenes and Events (DCASE) Workshop},
- year = 2024,
- month = oct,
- url = {https://www.merl.com/publications/TR2024-146}
- }
Li, Z., Lowy, A., Liu, J., Koike-Akino, T., Parsons, K., Malin, B., Wang, Y., "Analyzing Inference Privacy Risks Through Gradients In Machine Learning", ACM Conference on Computer and Communications Security (CCS), October 2024.
BibTeX TR2024-141 PDF
- @inproceedings{Li2024oct,
- author = {Li, Zhuohang and Lowy, Andrew and Liu, Jing and Koike-Akino, Toshiaki and Parsons, Kieran and Malin, Bradley and Wang, Ye}},
- title = {Analyzing Inference Privacy Risks Through Gradients In Machine Learning},
- booktitle = {ACM Conference on Computer and Communications Security (CCS)},
- year = 2024,
- month = oct,
- url = {https://www.merl.com/publications/TR2024-141}
- }
Hegde, D., Lohit, S., Peng, K.-C., Jones, M.J., Patel, V.M., "Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection", European Conference on Computer Vision (ECCV), Leonardis, A. and Ricci, E. and Roth, S. and Russakovsky, O. and Sattler, T. and Varol, G., Eds., DOI: 10.1007/978-3-031-73347-5_27, September 2024, pp. 475-491.
BibTeX TR2024-130 PDF Video Presentation
- @inproceedings{Hegde2024sep,
- author = {{Hegde, Deepti and Lohit, Suhas and Peng, Kuan-Chuan and Jones, Michael J. and Patel, Vishal M.}},
- title = {Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection},
- booktitle = {European Conference on Computer Vision (ECCV)},
- year = 2024,
- editor = {Leonardis, A. and Ricci, E. and Roth, S. and Russakovsky, O. and Sattler, T. and Varol, G.},
- pages = {475--491},
- month = sep,
- publisher = {Springer},
- doi = {10.1007/978-3-031-73347-5_27},
- issn = {0302-9743},
- isbn = {978-3-031-73346-8},
- url = {https://www.merl.com/publications/TR2024-130}
- }
Pais, G., Piedade, V., Chatterjee, M., Greiff, M., Miraldo, P., "A Probability-guided Sampler for Neural Implicit Surface Rendering", European Conference on Computer Vision (ECCV), September 2024.
BibTeX TR2024-129 PDF
- @inproceedings{Pais2024sep,
- author = {Pais, Goncalo and Piedade, Valter and Chatterjee, Moitreya and Greiff, Marcus and Miraldo, Pedro}},
- title = {A Probability-guided Sampler for Neural Implicit Surface Rendering},
- booktitle = {European Conference on Computer Vision (ECCV)},
- year = 2024,
- month = sep,
- url = {https://www.merl.com/publications/TR2024-129}
- }
Saijo, K., Wichern, G., Germain, F.G., Pan, Z., Le Roux, J., "TF-Locoformer: Transformer with Local Modeling by Convolution for Speech Separation and Enhancement", International Workshop on Acoustic Signal Enhancement (IWAENC), September 2024.
BibTeX TR2024-126 PDF Software
- @inproceedings{Saijo2024sep2,
- author = {Saijo, Kohei and Wichern, Gordon and Germain, François G and Pan, Zexu and Le Roux, Jonathan}},
- title = {TF-Locoformer: Transformer with Local Modeling by Convolution for Speech Separation and Enhancement},
- booktitle = {International Workshop on Acoustic Signal Enhancement (IWAENC)},
- year = 2024,
- month = sep,
- url = {https://www.merl.com/publications/TR2024-126}
- }
Cherian, A., Jain, S., Marks, T.K., "Few-shot Transparent Instance Segmentation for Bin Picking", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2024.
BibTeX TR2024-127 PDF
- @inproceedings{Cherian2024sep,
- author = {Cherian, Anoop and Jain, Siddarth and Marks, Tim K.}},
- title = {Few-shot Transparent Instance Segmentation for Bin Picking},
- booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
- year = 2024,
- month = sep,
- url = {https://www.merl.com/publications/TR2024-127}
- }