-
NEWS MERL contributes to IROS 2026 Date: September 27, 2026 - October 1, 2026
Where: The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
MERL Contacts: Siddarth Jain; Toshiaki Koike-Akino; Jing Liu; Daniel N. Nikovski; Arvind Raghunathan; Alexander Schperberg; Kei Suzuki; Ye Wang
Research Areas: Artificial Intelligence, Computer Vision, Control, Machine Learning, Optimization, Robotics, Signal ProcessingBrief- MERL made broad contributions to the technical program and robotics community at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026), held in Pittsburgh, Pennsylvania. MERL researchers presented one main-conference paper and five workshop papers, participated in an editorial board meeting, competed in the Humanoid IKEA Assembly Challenge, and contributed to the organization of an IROS workshop.
Main Conference Paper
- ORIGAMI: Object Representation Inferred Geometrically for Articulated ManIpulation, Yunfu Deng and Daniel N. Nikovski (TR2026-141)
The work introduces a geometric representation for articulated-object manipulation, enabling robots to infer compact representations of previously unknown articulated mechanisms for downstream learning and control.
Workshop Papers
- Deliberate Practice: Learning Robot Skills under a Budget Shivam Vats, Sudarshan Harithas, Mete Akbulut, Arvind Raghunathan, and George Konidaris (TR2026-147)
- ReCoVLA: VLM-Guided Reward Compilation for Failure Recovery in Vision-Language-Action Policies, Haodi Hu, Chung-Ta Huang, Jing Liu, Ye Wang, Kei Suzuki, Matthew Brand, and Toshiaki Koike-Akino (TR2026-148)
- Test-Time Attention: Can Robots Better Follow Commands? Jing Liu, Ye Wang, Kei Suzuki, and Toshiaki Koike-Akino (TR2026-142)
- Read the Manual: Grounding Behavior Tree Synthesis for Multi-Machine Factory Operation, Chak Lam Shek, Ye Wang, Jing Liu, Kei Suzuki, Pratap Tokekar, and Toshiaki Koike-Akino (TR2026-149)
- DYNAMIT: Dynamic Zero-Shot Manipulation via Instruction-Grounded Visual Tracking and Interception in Industrial Conveyor Environments, Harsh Singh, Kei Suzuki, Ye Wang, Jing Liu, Paola Cascante-Bonilla, and Toshiaki Koike-Akino (TR2026-146)
Together, these works address a range of challenges in modern robotics, including articulated-object manipulation, efficient robot skill learning, vision-language-action policies, test-time adaptation, autonomous factory operation, and dynamic manipulation. The paper on ReCoVLA was nominated as a spotlight talk.
Robotics Community Contributions
MERL Principal Research Scientist Dr. Siddarth Jain participated in the IEEE Robotics and Automation Letters (RA-L) editorial board meeting at IROS. Dr. Jain serves as an Associate Editor of RA-L, contributing to the peer-review and editorial activities of the robotics research community.
Former MERL scientist Dr. Diego Romeres was also among the organizers of the 1st International Workshop on Industrial Applications of Robot Learning (IARL 2026). The workshop brought together researchers from academia and industry to discuss how advances in robot learning can be translated into reliable and scalable real-world industrial robotic systems.
Humanoid IKEA Assembly Challenge
MERL also participated in the IROS 2026 Humanoid IKEA Assembly Challenge with Team MEL-Craft. The team achieved first place in the competition, demonstrating autonomous humanoid manipulation capabilities for a challenging furniture-assembly task. The MEL-Craft team included MERL researchers Kei Suzuki, Jing Liu, Alexander Schperberg, Toshiaki Koike-Akino, and Ye Wang, with contributions from MERL interns Haodi Hu, Harsh Singh, Chak Lam Shek, and Maxwell Asselmeier. The competition achievement is highlighted separately in MERL's related award announcement.
About IROS
The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) is a major international robotics conference bringing together researchers, engineers, and industry practitioners working across intelligent robots and systems. IROS 2026 took place in Pittsburgh from September 27 to October 1, 2026.
- MERL made broad contributions to the technical program and robotics community at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026), held in Pittsburgh, Pennsylvania. MERL researchers presented one main-conference paper and five workshop papers, participated in an editorial board meeting, competed in the Humanoid IKEA Assembly Challenge, and contributed to the organization of an IROS workshop.
-
TALK [MERL Seminar Series 2026] Yuanqi Du presents talk titled Towards Generalist Agents for Accelerating Scientific Discovery Date & Time: Wednesday, September 16, 2026; 12:00 PM
Speaker: Yuanqi Du, Microsoft Research New England
MERL Host: Pu (Perry) Wang
Research Areas: Artificial Intelligence, Machine LearningAbstract
Large language models (LLMs) are opening new frontiers in scientific research, enabling capabilities ranging from literature retrieval and hypothesis generation to experimental planning and operation, and bringing us closer to the vision of an AI Scientist. However, it remains unclear what principles should guide the development of such an AI Scientist. In this talk, I will ground this vision in the scientific discovery workflow that drives human progress. First, I will present quantitative evidence of how LLMs are changing the way we search for and validate hypotheses, and how they can serve as powerful tools for hypothesis search. Next, I will discuss how automation can enter the loop by steering the decision-making process. Finally, I will discuss the path toward truly autonomous agents that can operate without any human intervention. These generalist agents are being validated across problems in chemistry, biology, and materials science, including through wet-lab experiments.
-
TALK [MERL Seminar Series 2026] Tess Smidt presents talk titled Adventures in Building Structure into Models: Lessons from Constructing Euclidean Neural Networks for Physics Date & Time: Wednesday, August 19, 2026; 11:00 AM
Speaker: Tess Smidt, MIT
MERL Host: Suhas Lohit
Research Areas: Artificial Intelligence, Machine LearningAbstract
Symmetry provides a powerful lens for building machine learning models that interact with scientific data. Euclidean neural networks (E(3)NNs) make this concrete: architectures that encode transformation laws through group representations, enabling models to operate on geometric and tensorial data while respecting the structure of physical systems. In this talk, I’ll share lessons from building and applying these models in practice. Incorporating symmetry shapes how data is represented, how models learn, and how they are optimized, while introducing new trade-offs in expressivity and computation.
-
NEWS MERL Presents Five Papers at IEEE Quantum Week 2026 Date: September 13, 2026 - September 18, 2026
Where: Toronto, Canada
MERL Contact: Toshiaki Koike-Akino
Research Areas: Applied Physics, Artificial Intelligence, Machine Learning, Optimization, Signal ProcessingBrief- MERL is pleased to announce that five papers have been accepted to the 2026 IEEE International Conference on Quantum Computing and Engineering (QCE), also known as IEEE Quantum Week 2026, held September 13–18, 2026, in Toronto, Canada.
The papers highlight MERL’s recent advances in quantum computing, spanning hardware-efficient quantum state preparation, quantum low-density parity-check (QLDPC) code design, graph-cover-based code construction, machine-learning-assisted code search, and reinforcement-learning-guided quantum error correction. Together, these works address important challenges toward more efficient and reliable quantum computing systems.
The five papers are:
- “Near-Lower-Bound Approximate Quantum State Preparation with Hardware-Efficient Circuits” — Toshiaki Koike-Akino (TR2026-131)
- “Reinforcement-Learning-Guided Multi-Branch Decoding of Quantum LDPC Codes” — Vahid Nourozi, Toshiaki Koike-Akino, and David Mitchell (TR2026-130)
- “Q-Learning Base Search Voltage-Labeled Covers for Weight-Six Bivariate-Bicycle Quantum LDPC Codes” — Vahid Nourozi, David Mitchell, and Toshiaki Koike-Akino (TR2026-132)
- “Collision-Voltage Design of Directional Covers for Bivariate Bicycle Quantum LDPC Codes” — Vahid Nourozi, David Mitchell, and Toshiaki Koike-Akino (TR2026-133)
- “Base-Preserving APM/Voltage Lifts of Bivariate Bicycle Quantum LDPC Codes” — Vahid Nourozi, David Mitchell, and Toshiaki Koike-Akino (TR2026-129)
- MERL is pleased to announce that five papers have been accepted to the 2026 IEEE International Conference on Quantum Computing and Engineering (QCE), also known as IEEE Quantum Week 2026, held September 13–18, 2026, in Toronto, Canada.
-
AWARD MERL Team Wins Real-TSE Challenge Track 2 on Offline Target Speaker Extraction Date: July 6, 2026
Awarded to: Dominik Klement, Yoshiki Masuyama, Christoph Boeddeker, Kohei Saijo, Julius Richter, Gordon Wichern, and Jonathan Le Roux
MERL Contacts: Christoph Boeddeker; Jonathan Le Roux; Yoshiki Masuyama; Julius Richter; Gordon Wichern
Research Areas: Artificial Intelligence, Machine Learning, Speech & AudioBriefMERL's Speech & Audio team, led by MERL intern Dominik Klement, ranked 1st out of 11 teams in Track 2, "Offline Target Speaker Extraction," of the Real-TSE Challenge. The challenge focuses on target speaker extraction (TSE) from real-world conversational recordings in either English or Chinese, where the goal is to extract the speech of a target speaker in the presence of interfering speakers, background noise, and reverberation.
While modern TSE systems have achieved strong performance on simulated speech mixtures, their performance can degrade considerably on real-world recordings due to the mismatch between simulated training data and actual conversational environments. The Real-TSE Challenge was designed to advance TSE under these realistic conditions, using real far-field conversational recordings for evaluation.
The MERL team won Track 2 by focusing on training data and curriculum learning rather than introducing a new model architecture. Starting from a strong speech separation model, the team progressively trained the system on fully overlapping synthetic speech, simulated conversations, realistic far-field mixtures, and finally real conversational recordings. This approach reduced the token error rate (TER), measured at either the word (English) or character (Chinese) level, from 70% to 37% on the development set and achieved a final TER of 61.3% on the evaluation set, best among the 11 participating teams. The team also topped the leaderboard in terms of the aggregate ranking across the four measures evaluating intelligibility, target speaker presence rate, speaker similarity, and perceptual quality.
The team also investigated the reliability of the challenge metrics and demonstrated that neural network-based speaker similarity and predicted speech-quality scores could be substantially improved without a corresponding improvement in perceptual quality. Because learned metrics can be susceptible to adversarial attacks or optimization that exploits weaknesses in the metric itself, these findings highlight both the importance of realistic training data for real-world TSE and the need for robust evaluation metrics when developing speech extraction systems.
A paper summarizing the team's findings will be presented at the IEEE Spoken Language Technology (SLT) 2026 workshop, to be held in Palermo, Italy from December 13-16, 2026.
REAL-TSE Challenge: Track 2 rankings — Offline Target Speaker Extraction Rank Team TER ↓ F1 ↑ SIM ↑ P808 ↑ Score ↓ 1 MERL 0.613 (1) 0.861 (2) 0.538 (3) 3.371 (2) 2.00 2 YiJiaHe 0.639 (2) 0.871 (1) 0.565 (1) 3.128 (9) 3.25 3 CARTSE 0.651 (3) 0.857 (4) 0.544 (2) 3.138 (8) 4.25 4 WasedaM 0.675 (5) 0.858 (3) 0.480 (6) 3.232 (6) 5.00 5 SonicAGI 0.680 (6) 0.851 (6) 0.471 (7) 3.258 (5) 6.00 6 WAKA 0.670 (4) 0.847 (8) 0.471 (7) 3.150 (7) 6.50 6 SHNU-TSE 0.731 (9) 0.840 (9) 0.507 (5) 3.362 (3) 6.50 7 ChuEst 0.710 (7) 0.831 (11) 0.532 (4) 3.064 (10) 8.00 8 pyannoteAI 0.728 (8) 0.855 (5) 0.464 (9) 2.904 (12) 8.50 9 AGH-JHU 0.743 (10) 0.837 (10) 0.434 (11) 3.335 (4) 8.75 10 WHU_IASP 0.757 (11) 0.850 (7) 0.465 (8) 2.961 (11) 9.25 11 CUDA_OUT_OF_MEMORY 0.827 (12) 0.819 (13) 0.364 (13) 3.435 (1) 9.75 12 BSRNN_EMB Baseline 0.829 (13) 0.829 (12) 0.417 (12) 2.875 (13) 12.50 12 BSRNN_TFMAP Baseline 0.838 (14) 0.829 (12) 0.443 (10) 2.756 (14) 12.50 ↓ Lower is better; ↑ higher is better. Parentheses show metric ranks. The score is the average of the four dense metric ranks; tied scores share a position. Best metric values are bold. P808 denotes DNSMOS-P808.
Source: Official REAL-TSE Challenge rankings. BSRNN entries are organizer baselines.
-
AWARD MERL Team Wins DCASE 2026 Challenge on Anomalous Sound Detection for Machine Condition Monitoring Date: June 30, 2026
Awarded to: Takuya Fujimura, Gordon Wichern, Yoshiki Masuyama, Christoph Boeddeker, Kohei Saijo, Julius Richter, Takahiro Edo, and Jonathan Le Roux
MERL Contacts: Christoph Boeddeker; Jonathan Le Roux; Yoshiki Masuyama; Julius Richter; Gordon Wichern
Research Areas: Artificial Intelligence, Machine Learning, Signal Processing, Speech & AudioBrief- MERL's Speech & Audio team ranked 1st out of 51 teams in the DCASE 2026 Challenge’s Task 2, “Noise-aware Unsupervised Anomalous Sound Detection for Machine Condition Monitoring.” The team was led by MERL intern Takuya Fujimura, and also included Gordon Wichern, Yoshiki Masuyama, Christoph Boeddeker, Kohei Saijo, Julius Richter, Takahiro Edo, and Jonathan Le Roux.
The IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE Challenge), started in 2013, has been organized yearly since 2016, and gathers challenges on multiple tasks related to the detection, analysis, and generation of sound events. This year, the DCASE 2026 Challenge received 421 submissions from 135 teams across seven tasks.
The MERL team won Task 2, Noise-aware Unsupervised Anomalous Sound Detection for Machine Condition Monitoring, which aims at building noise-robust systems for automatically detecting machine failure via microphones when only normal machine operating data is available for system development. Task 2 was by far the most popular out of the 7 DCASE 2026 tasks, with 51 teams submitting 168 entries. The MERL team's system was built around MERL’s recently proposed paradigm of noise-aware self-supervised learning, which extracts noise robust features leveraging two-channel recordings, in which one microphone is used to capture noise. Anomaly detection is then performed in the extracted denoised feature space using advanced score normalization. The team's best submission obtained a composite score of 70.24% on five evaluation machines, largely outperforming the 2nd best team's 65.45%.
MERL also participated in Task 4, Spatial Semantic Segmentation of Sound Scenes (S5) and placed 3rd out of 10 teams in separation performance. Our cascaded system consists of universal sound separation with source counting, source classification, and class-aware refinement, where the separation and refinement modules are built upon MERL's TF-Locoformer separation technology. Notably, the team's best submission obtained a label prediction accuracy of 76.92% on the evaluation set, largely outperforming the 2nd best team's 65.54%.
- MERL's Speech & Audio team ranked 1st out of 51 teams in the DCASE 2026 Challenge’s Task 2, “Noise-aware Unsupervised Anomalous Sound Detection for Machine Condition Monitoring.” The team was led by MERL intern Takuya Fujimura, and also included Gordon Wichern, Yoshiki Masuyama, Christoph Boeddeker, Kohei Saijo, Julius Richter, Takahiro Edo, and Jonathan Le Roux.
-
NEWS MERL Presents 4 Main Conference Papers and 6 Workshop Papers at ICML 2026 Date: July 6, 2026 - July 11, 2026
Where: COEX, Seoul, South Korea
MERL Contacts: Moitreya Chatterjee; Anoop Cherian; Stefano Di Cairano; Toshiaki Koike-Akino; Christopher R. Laughman; Jing Liu; Suhas Lohit; Kuan-Chuan Peng; Alexander Schperberg; Ye Wang; Gordon Wichern
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Signal ProcessingBrief- MERL researchers are proud to present 4 main conference papers and 6 workshop papers at ICML 2026. ICML, taking place from July 6-11 in Seoul, South Korea, is a premier international conference in machine learning.
Main Conference Papers with MERL Authors:
1. Understanding Dynamic Compute Allocation in Recurrent Transformers by Ibraheem Muhammad Moosa, Suhas Lohit, Ye Wang, Moitreya Chatterjee, and Wenpeng Yin.
2. LLawCo: Learning Laws of Cooperation for Modeling Embodied Multi-Agent Behavior by Qinhong Zhou, Chuang Gan, and Anoop Cherian.
3. Memory-Distilled Selection for Noise-Robust Anomaly Detection by Sirojbek Safarov, Jaewoo Park, Yoon G. Jung, Kuan-Chuan Peng, Wonchul Kim, Seongdeok Bang, and Octavia Camps.
4. Partial Ring Scan: Revisiting Scan Order in Vision State Space Models by Yi-Kuan Hsieh, Kuan-Chuan Peng, Xin Li, Ming-Ching Chang, Yu-Chee Tseng, and Jun-Wei Hsieh.
Workshop Papers with MERL Authors:
1. WISE: Weighted Iterative Society-of-Experts for Multimodal Multi-Agent Debate with Probabilistic Consensus by Anoop Cherian, Suhas Lohit, and Kuan-Chuan Peng. (Workshop on Scalable Learning and Optimization for Efficient Multimodal AI Agents (SCALE))
2. MIRROR: Multisensory Implicit Rejection-sampled RObotic policy by Amisha Bhaskar, Pratap Tokekar, Stefano Di Cairano, and Alexander Schperberg. (Workshop on Structured Probabilistic Inference & Generative Modeling)
3. Reinforced Neural Processes: Memory-Efficient Time-Series Forecasting with a World-Feedback-Trained Memory Policy by Nibraas Khan, Gordon Wichern, and Christopher R. Laughman. (Workshop on Reinforcement Learning from World Feedback (RLxF))
4. Connecting Low-Rank Adapters and Policy Stability in GRPO Fine-Tuning by Antonin Rottman, Francesco Tonin, Yongtao Wu, Toshiaki Koike-Akino, and Volkan Cevher. (Workshop on Connecting Low-rank Representations in AI (CoLorAI))
5. EinSort: Sorting is All We Need for Tensorizing LLM by Toshiaki Koike-Akino, Jing Liu, and Ye Wang. (Workshop on Connecting Low-rank Representations in AI (CoLorAI))
6. Temper and Tilt Lead to SLOP: Reward Hacking Mitigation with Inference-Time Alignment by Ye Wang, and Jing Liu, and Toshiaki Koike-Akino. (Workshop on Agents in the Wild: Safety, Security, and Beyond)
- MERL researchers are proud to present 4 main conference papers and 6 workshop papers at ICML 2026. ICML, taking place from July 6-11 in Seoul, South Korea, is a premier international conference in machine learning.
-
NEWS MERL researchers present 9 papers at IEEE ICRA 2026 Date: June 1, 2026 - June 5, 2026
Where: Vienna, Austria
MERL Contacts: Radu Corcodel; Stefano Di Cairano; Purnanand Elango; Siddarth Jain; Alexander Schperberg; Kento Tomita
Research Areas: Artificial Intelligence, Computer Vision, Control, Dynamical Systems, Machine Learning, Optimization, RoboticsBrief- MERL researchers presented nine papers at the recently concluded IEEE International Conference on Robotics and Automation (ICRA) 2026 in Vienna, Austria. The papers covered a broad set of topics in robotics, including robot perception, visuo-tactile sensing, contact and pose estimation, manipulation, reinforcement learning, diffusion policies, loco-manipulation, contact-implicit trajectory optimization, legged locomotion, localization, and perception-aware planning.
IEEE ICRA is the flagship conference of the IEEE Robotics and Automation Society and the world’s largest and most comprehensive technical conference focused on research advances and the latest technological developments in robotics. The event attracts nearly 8,000 participants and receives more than 5,000 paper submissions.
- MERL researchers presented nine papers at the recently concluded IEEE International Conference on Robotics and Automation (ICRA) 2026 in Vienna, Austria. The papers covered a broad set of topics in robotics, including robot perception, visuo-tactile sensing, contact and pose estimation, manipulation, reinforcement learning, diffusion policies, loco-manipulation, contact-implicit trajectory optimization, legged locomotion, localization, and perception-aware planning.
-
NEWS MERL Presents 7 Papers and 2 Workshops at CVPR 2026 Date: June 3, 2026 - June 7, 2026
Where: Colorado Convention Center, Denver, Colorado
MERL Contacts: Moitreya Chatterjee; Anoop Cherian; Suhas Lohit; Lalit Manam; Tim K. Marks; Pedro Miraldo; Kuan-Chuan Peng
Research Areas: Artificial Intelligence, Computer Vision, Machine LearningBrief- MERL researchers are proud to present 7 papers, including two highlight papers (top 3.6% of submissions), and 2 workshops at CVPR 2026. CVPR, taking place from June 3-7 in Denver, CO, USA, is a premier international conference in computer vision.
Papers with MERL Authors:
1. Point4Cast: Streaming Dynamic Scene Reconstruction and Forecasting by Xinhang Liu, Pedro Miraldo, Suhas Lohit, Huaizu Jiang, Naoko Sawada, Yu-Wing Tai, Chi-Keung Tang, and Moitreya Chatterjee (Highlight Paper)
2. Parallel Rigidity Matters for Bundle Adjustment by Lalit Manam and Venu Govindu (Highlight Paper)
3. Revisiting Monocular SLAM with Spatio-Temporal Scene Modeling by Valter Piedade, Lalit Manam, Masashi Yamazaki, and Pedro Miraldo
4. AssemblyBench: Physics-Aware Assembly of Complex Industrial Objects by Danrui Li, Jiahao Zhang, Bernhard Egger, Moitreya Chatterjee, Suhas Lohit, Tim K. Marks, and Anoop Cherian
5. LASER: Layer-wise Scale Alignment for Training-Free Streaming 4D Reconstruction by Tianye Ding, Yiming Xie, Yiqing Liang, Moitreya Chatterjee, Pedro Miraldo, and Huaizu Jiang
6. SoREL: Soft-Label Refurbishment with Ensemble Learning for Noisy Long-Tailed Classification by Jun-Wei Hsieh, Ying-Hsuan Wu, Yi-Kuan Hsieh, Xin Li, Kuan-Chuan Peng, Ming-Ching Chang (CVPR Findings paper)
7. MMHOI: Complex 3D Multi-Human-Object Interaction Understanding by Kaen Kogashi and Anoop Cherian (PhysHuman Workshop paper)
Workshops Co-Organized by MERL:
1. Multimodal Algorithmic Reasoning Workshop by Anoop Cherian, Suhas Lohit, Kuan-Chuan Peng, Honglu Zhou, Kevin Smith, and Josh Tenenbaum
2. The Third Workshop on Anomaly Detection with Foundation Models by Kuan-Chuan Peng, Ying Zhao, and Abhishek Aich
- MERL researchers are proud to present 7 papers, including two highlight papers (top 3.6% of submissions), and 2 workshops at CVPR 2026. CVPR, taking place from June 3-7 in Denver, CO, USA, is a premier international conference in computer vision.
-
EVENT MERL Contributes to ICASSP 2026 Date: Monday, May 4, 2026 - , May 8, 2026
Location: Barcelona, Spain
MERL Contacts: Wael H. Ali; Petros T. Boufounos; Chiori Hori; Jonathan Le Roux; Yanting Ma; Hassan Mansour; Yoshiki Masuyama; Joshua Rapp; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern
Research Areas: Artificial Intelligence, Computational Sensing, Computer Vision, Machine Learning, Optimization, Signal Processing, Speech & AudioBrief- MERL has made numerous contributions to both the organization and technical program of ICASSP 2026, which is being held in Barcelona, Spain from May 4-8, 2026.
Sponsorship
MERL is proud to be a Silver Patron of the conference and will participate in the student job fair on Thursday, May 7. Please join this session to learn more about employment opportunities at MERL, including openings for research scientists, post-docs, and interns. MERL Distinguished Research Scientists Petros T. Boufounos and Jonathan Le Roux will also present a spotlight session on MERL’s research in signal processing on Tuesday, May 5 at 13:05. Finally, MERL will sponsor a photo booth on Thursday, May 7 and Friday, May 8, where ICASSP participants can take professional photos with friends and colleagues, which will be emailed to them.
MERL is also pleased to be the sponsor of two IEEE Awards that will be presented at the conference. We congratulate Prof. Nasir Ahmed, the recipient of the 2026 IEEE Fourier Award for Signal Processing, and Dr. Alex Acero, the recipient of the 2026 IEEE James L. Flanagan Speech and Audio Processing Award.
Technical Program
MERL is presenting 8 papers in the main conference on a wide range of topics including source separation, spatial audio, neural audio codecs, radar-based pose estimation, camera-based airflow sensing, radar array processing, and optimization. Another paper on neural speech codecs will be presented at the Low-Resource Audio Codec (LRAC) Satellite Workshop. MERL researchers will also present two articles published in IEEE Open Journal of Signal Processing (OJSP) on music source separation and head-related transfer function (HRTF) modeling. Finally, Speech and Audio Team members Yoshiki Masuyama and Jonathan Le Roux co-organized a Special Session on Neural Spatial Audio Processing, which will feature six oral presentations.
About ICASSP
ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 4000 participants each year.
- MERL has made numerous contributions to both the organization and technical program of ICASSP 2026, which is being held in Barcelona, Spain from May 4-8, 2026.
-
TALK [MERL Seminar Series 2026] Jialong Wu presents talk titled World Models and Human-like Reasoning Date & Time: Wednesday, March 25, 2026; 11:00 AM
Speaker: Jialong Wu, Tsinghua University
MERL Host: Anoop Cherian
Research Areas: Artificial Intelligence, Computer Vision, Machine LearningAbstract
This talk introduces the background and key findings of our recent work, "Visual Generation Unlocks Human-Like Reasoning through Multimodal World Models," which answers the question of when and how visual generation enabled by unified multimodal models (UMMs) benefits reasoning. We take a world model perspective, inspired by human cognition. Specifically, humans construct mental models of the world, representing information and knowledge through two complementary channels—verbal and visual—to support reasoning, planning, and decision-making. In contrast, recent advances in large language models (LLMs) and vision–language models (VLMs) largely rely on verbal chain-of-thought reasoning, leveraging primarily symbolic and linguistic world knowledge. Unified multimodal models (UMMs) open a new paradigm by using visual generation for visual world modeling, advancing more human-like reasoning on tasks grounded in the physical world. In this work, we formalize the atomic capabilities of world models and world model-based chain-of-thought reasoning. We highlight the richer informativeness and complementary prior knowledge afforded by visual world modeling, leading to our visual superiority hypothesis for tasks grounded in the physical world. We identify and design tasks that necessitate interleaved visual-verbal CoT reasoning, constructing a new evaluation suite, VisWorld-Eval. Through controlled experiments on BAGEL, we show that interleaved CoT significantly outperforms purely verbal CoT on tasks that favor visual world modeling, strongly supporting our insights.
-
NEWS MERL hosts Boston AI Music Meetup Date: March 19, 2026
Where: Cambridge, MA
MERL Contact: Gordon Wichern
Research Areas: Artificial Intelligence, Machine Learning, Speech & AudioBrief- MERL hosted the Boston AI Music Meetup on March 19, 2026, bringing together researchers, musicians, and technologists from the local community to explore the intersection of artificial intelligence and music. The event featured talks on emerging approaches in AI-driven audio and creative tools, including a presentation by Elena Georgieva (NYU MARL) on improving audio quality for singing and speech using CLAP-based methods, as well as a talk by Ashvala Vinay (NoneType) on creative workflows using infinite canvas systems. Following the presentations, attendees participated in a networking session, fostering discussion and collaboration across academia and industry.
The Boston AI Music Meetup has been held monthly since 2024 (including a presentation on MERL’s music source separation work in May 2025), and has grown to include over 1,200 subscribers, attracting attendees from across the Northeast. It provides a forum for knowledge exchange and collaboration within the rapidly evolving AI music ecosystem, with discussions spanning music information retrieval, generative AI, and machine learning for creative practice.
- MERL hosted the Boston AI Music Meetup on March 19, 2026, bringing together researchers, musicians, and technologists from the local community to explore the intersection of artificial intelligence and music. The event featured talks on emerging approaches in AI-driven audio and creative tools, including a presentation by Elena Georgieva (NYU MARL) on improving audio quality for singing and speech using CLAP-based methods, as well as a talk by Ashvala Vinay (NoneType) on creative workflows using infinite canvas systems. Following the presentations, attendees participated in a networking session, fostering discussion and collaboration across academia and industry.
-
NEWS Toshiaki Koike-Akino delivers an invited talk as a panelist at OFC 2026 Date: March 17, 2026
MERL Contact: Toshiaki Koike-Akino
Research Areas: Artificial Intelligence, Communications, Machine Learning, Signal ProcessingBrief- MERL researcher Toshiaki Koike-Akino will serve as a panelist at OFC 2026, the premier global event for optical communications and networking, to be held in Los Angeles, March 15–19.
Dr. Koike-Akino will participate in the special panel session titled “Machine Learning is Taking Over Optical Communications—But Which Algorithms Should We Use?” He will deliver a panel talk titled “Scaling AI with Light: AI Is Taking Over Optics — But Optics May Take Over AI.” His talk will discuss the growing synergy between AI and optical technologies, highlighting the emerging vision of leveraging optical physics not only as an application domain for AI, but also as a platform for scaling future AI systems.
- MERL researcher Toshiaki Koike-Akino will serve as a panelist at OFC 2026, the premier global event for optical communications and networking, to be held in Los Angeles, March 15–19.
-
TALK [MERL Seminar Series 2026] Alex Gu presents talk titled Proving and Improving: Language Models for Theorem Proving and Proof Shortening in Lean Date & Time: Wednesday, February 11, 2026; 1:00 PM
Speaker: Alex Gu, MIT
MERL Host: Pu (Perry) Wang
Research Areas: Artificial Intelligence, Machine Learning, OptimizationAbstract
Large language models (LLMs) have made steady progress in formal mathematics, achieving near–International Mathematical Olympiad (IMO) performance. This talk presents two complementary advances toward more capable and interpretable formal proving systems. First, we introduce LeanDojo, a foundational open-source toolkit bridging ML and Lean, enabling large-scale data extraction, interactive training, and the development of ReProver, a retrieval-augmented Lean prover. Next, we turn to a critical challenge: proofs produced by LLMs are often unnecessarily long, redundant, and opaque. To mitigate this, we introduce ProofOptimizer, a system that automatically simplifies Lean proofs while preserving correctness. It combines symbolic linting, a fine-tuned 7B model, and iterative refinement, reducing proof length by up to 87% on MiniF2F and 57% on PutnamBench, even halving some IMO-level proofs. Together, these systems demonstrate how AI can make automated proofs not only possible, but also increasingly comprehensible.
-
TALK [MERL Seminar Series 2026] Laixi Shi presents talk titled Robust Decision Making Without Compromising Learning Efficiency Date & Time: Wednesday, January 14, 2026; 1:00 PM
Speaker: Laixi Shi, Johns Hopkins University
MERL Host: Dehong Liu
Research Areas: Artificial Intelligence, Control, Machine LearningAbstract
Decision-making artificial intelligence (AI) has revolutionized human life ranging from healthcare, daily life, to scientific discovery. However, current AI systems often lack reliability and are highly vulnerable to small changes in complex, interactive, and dynamic environments. My research focuses on achieving both reliability and learning efficiency simultaneously when building AI solutions. These two goals seem conflicting, as enhancing robustness against variability often leads to more complex problems that requires more data and computational resources, at the cost of learning efficiency. But does it have to?
In this talk, I overview my work on building reliable decision-making AI without sacrificing learning efficiency, offering insights into effective optimization problem design for reliable AI. To begin, I will focus on reinforcement learning (RL) — a key framework for sequential decision-making, and demonstrate how distributional robustness can be achieved provably without paying statistical premium (additional training data cost) compared to non-robust counterparts. Next, shifting to decision-making in strategic multi-agent systems, I will demonstrate that incorporating realistic risk preferences—a key feature of human decision-making—enables computational tractability, a benefit not present in traditional models. Finally, I will present a vision for building reliable, learning-efficient AI solutions for human-centered applications, though agentic and multi-agentic AI systems.
-
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
Research Areas: Artificial Intelligence, Machine Learning, RoboticsBrief- 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.
- 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.
-
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 Lohit; Pedro Miraldo; Saviz Mowlavi; Kuan-Chuan Peng; Arvind Raghunathan; 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 & AudioBrief- 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).
- MERL researchers presented 2 main-conference papers and 5 workshop papers, as well as organized a workshop, at NeurIPS 2025.
-
EVENT SANE 2025 - Speech and Audio in the Northeast Date: Friday, November 7, 2025
Location: Google, New York, NY
MERL Contacts: Jonathan Le Roux; Yoshiki Masuyama
Research Areas: Artificial Intelligence, Machine Learning, Speech & AudioBrief- SANE 2025, a one-day event gathering researchers and students in speech and audio from the Northeast of the American continent, was held on Friday November 7, 2025 at Google, in New York, NY.
It was the 12th edition in the SANE series of workshops, which started in 2012 and is typically held every year alternately in Boston and New York. Since the first edition, the audience has grown to about 200 participants and 50 posters each year, and SANE has established itself as a vibrant, must-attend event for the speech and audio community across the northeast and beyond.
SANE 2025 featured invited talks by six leading researchers from the Northeast as well as from the wider community: Dan Ellis (Google Deepmind), Leibny Paola Garcia Perera (Johns Hopkins University), Yuki Mitsufuji (Sony AI), Julia Hirschberg (Columbia University), Yoshiki Masuyama (MERL), and Robin Scheibler (Google Deepmind). It also featured a lively poster session with 50 posters.
MERL Speech and Audio Team's Yoshiki Masuyama presented a well-received overview of the team's recent work on "Neural Fields for Spatial Audio Modeling". His talk highlighted how neural fields are reshaping spatial audio research by enabling flexible, data-driven interpolation of head-related transfer functions and room impulse responses. He also discussed the integration of sound-propagation physics into neural field models through physics-informed neural networks, showcasing MERL’s advances at the intersection of acoustics and deep learning.
SANE 2025 was co-organized by Jonathan Le Roux (MERL), Quan Wang (Google Deepmind), and John R. Hershey (Google Deepmind). SANE remained a free event thanks to generous sponsorship by Google, MERL, Apple, Bose, and Carnegie Mellon University.
Slides and videos of the talks are available from the SANE workshop website and via a YouTube playlist.
- SANE 2025, a one-day event gathering researchers and students in speech and audio from the Northeast of the American continent, was held on Friday November 7, 2025 at Google, in New York, NY.
-
NEWS Abraham Vinod Delivers Invited Talks at The University of Texas at Austin and The University of Texas at Dallas Date: November 11, 2025 - November 13, 2025
MERL Contact: Abraham P. Vinod
Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Optimization, RoboticsBrief- MERL researcher Abraham Vinod was invited to present MERL's latest research at the University of Texas at Austin and The University of Texas at Dallas this November. His talk discussed a tractable set-based method for a broad class of robust control problems with nonlinear dynamics and bounded uncertainty, with applications to powered descent guidance and drone motion planning problems. Additionally, he also presented MERL's recent research on environmental monitoring using hetereogenous robots, with applications in disaster management and search-and-rescue.
-
NEWS Jonathan Le Roux Elected Vice Chair and Gordon Wichern Reelected as Member of the IEEE AASP Technical Committee Date: November 14, 2025
MERL Contacts: Jonathan Le Roux; Gordon Wichern
Research Areas: Artificial Intelligence, Machine Learning, Speech & AudioBrief- Two members of MERL’s Speech and Audio Team have been elected to important positions within the IEEE Audio and Acoustic Signal Processing Technical Committee (AASP TC), a leading body of the IEEE Signal Processing Society that brings together experts from academia and industry working on speech, music, environmental audio, spatial acoustics, enhancement, separation, and machine learning for audio. The committee plays a central role in guiding the scientific direction of the field by promoting emerging research areas, shaping major conferences such as ICASSP and WASPAA, organizing special sessions and tutorials, and fostering a vibrant and collaborative global community.
Jonathan Le Roux, Senior Team Leader and Distinguished Research Scientist, has been elected as the next Vice Chair of the AASP TC. His election reflects his longstanding contributions to the audio and acoustic signal processing community, his leadership in workshop and conference organization, and his significant impact across a wide range of research areas within the TC’s scope. Jonathan will serve a one-year term as Vice Chair, after which he will succeed Prof. Minje Kim (UIUC) as Chair of the AASP TC for a two-year term in 2027–28, helping steer the committee’s strategic initiatives and continued growth.
During the same election, Senior Principal Research Scientist Gordon Wichern, who currently serves as Chair of the Review Subcommittee, was reelected for a second three-year term as a member of the AASP TC, serving from 2026 to 2028. His continued presence on the committee reflects his impactful research and active service to the audio and acoustic signal processing community.
- Two members of MERL’s Speech and Audio Team have been elected to important positions within the IEEE Audio and Acoustic Signal Processing Technical Committee (AASP TC), a leading body of the IEEE Signal Processing Society that brings together experts from academia and industry working on speech, music, environmental audio, spatial acoustics, enhancement, separation, and machine learning for audio. The committee plays a central role in guiding the scientific direction of the field by promoting emerging research areas, shaping major conferences such as ICASSP and WASPAA, organizing special sessions and tutorials, and fostering a vibrant and collaborative global community.
-
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 & AudioBrief- 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.
- 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.
-
NEWS MERL Papers, Workshops, and Talks at ICCV 2025 Date: October 19, 2025 - October 23, 2025
Where: Honolulu, HI, USA
MERL Contacts: Petros T. Boufounos; Anoop Cherian; Toshiaki Koike-Akino; Hassan Mansour; Tim K. Marks; Pedro Miraldo; Kuan-Chuan Peng; Pu (Perry) Wang
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Signal ProcessingBrief- MERL researchers presented 3 conference papers and 3 workshop papers, co-organized 2 workshops, and delivered 2 invited talks at the IEEE International Conference on Computer Vision (ICCV) 2025, which was held in Honolulu, HI, USA from October 19-23, 2025. ICCV is one of the most prestigious and competitive international conferences in the area of computer vision. Details of MERL contributions are provided below:
Main Conference Papers:
1. "SAC-GNC: SAmple Consensus for adaptive Graduated Non-Convexity" by V. Piedade, C. Sidhartha, J. Gaspar, V. M. Govindu, and P. Miraldo. (Highlight Paper)
Paper: https://www.merl.com/publications/TR2025-146
2. "Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts" by C.-A. Yang, K.-C. Peng, and R. A. Yeh.
Paper: https://www.merl.com/publications/TR2025-124
3. "Manual-PA: Learning 3D Part Assembly from Instruction Diagrams" by J. Zhang, A. Cherian, C. Rodriguez-Opazo, W. Deng, and S. Gould.
Paper: https://www.merl.com/publications/TR2025-139
MERL Co-Organized Workshops:
1. "The Workshop on Anomaly Detection with Foundation Models (ADFM)" by K.-C. Peng, Y. Zhao, and A. Aich.
Workshop link: https://adfmw.github.io/iccv25/
2. "The 8th International Workshop on Computer Vision for Physiological Measurement (CVPM)" by D. McDuff, W. Wang, S. Stuijk, T. Marks, H. Mansour, V. R. Shenoy.
Workshop link: https://sstuijk.estue.nl/cvpm/cvpm25/
MERL Keynote Talks at Workshops:
1. Tim K. Marks, Keynote Speaker at the Workshop on Computer Vision for Physiological Measurement (CVPM).
Workshop website: https://vineetrshenoy.github.io/cvpmSeptember2025/
2. Tim K. Marks, Keynote Speaker at the Workshop on Analysis and Modeling of Faces and Gestures (AMFG).
Workshop website: https://fulab.sites.northeastern.edu/amfg2025/
Workshop Papers:
1. "Joint Training of Image Generator and Detector for Road Defect Detection" by K.-C. Peng.
paper: https://www.merl.com/publications/TR2025-149
2. "Radar-Conditioned 3D Bounding Box Diffusion for Indoor Human Perception" by R. Yataka, P. Wang, P.T. Boufounos, and R. Takahashi.
paper: https://www.merl.com/publications/TR2025-154
3. "L-GGSC: Learnable Graph-based Gaussian Splatting Compression" by S. Kato, T. Koike-Akino, and T. Fujihashi.
paper: https://www.merl.com/publications/TR2025-148
- MERL researchers presented 3 conference papers and 3 workshop papers, co-organized 2 workshops, and delivered 2 invited talks at the IEEE International Conference on Computer Vision (ICCV) 2025, which was held in Honolulu, HI, USA from October 19-23, 2025. ICCV is one of the most prestigious and competitive international conferences in the area of computer vision. Details of MERL contributions are provided below:
-
NEWS Diego Romeres Delivers Invited Talks at Fraunhofer Italia and the University of Padua Date: July 16, 2025 - July 18, 2025
Research Areas: Artificial Intelligence, Control, Machine Learning, Optimization, Robotics, Human-Computer InteractionBrief- MERL researcher Diego Romeres was invited to present MERL's latest research at two institutions in Italy this July, focusing on human-robot collaboration and LLM-driven assembly systems.
On July 16th, Dr. Romeres delivered a talk titled “Human-Robot Collaborative Assembly” at Fraunhofer Italia – Innovation Engineering Center (EIC) in Bolzano. His presentation showcased research on human-robot collaboration for efficient and flexible assembly processes. Fraunhofer Italia EIC is a non-profit research institute focused on enabling digital and sustainable transformation through applied innovation in close collaboration with both public and private sectors.
Two days later, on July 18th, Dr. Romeres was hosted by the University of Padua, one of Europe’s oldest and most renowned universities. His invited lecture, “Robot Assembly through Human Collaboration & Large Language Models”, explored how artificial intelligence can enhance human-robot synergy in complex assembly tasks.
- MERL researcher Diego Romeres was invited to present MERL's latest research at two institutions in Italy this July, focusing on human-robot collaboration and LLM-driven assembly systems.
-
NEWS MERL researchers present 13 papers at ACC 2025 Date: July 8, 2025 - July 10, 2025
Where: Denver, USA
MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; Purnanand Elango; Jordan Leung; Saviz Mowlavi; Abraham P. Vinod; Yebin Wang; Avishai Weiss
Research Areas: Control, Dynamical Systems, Electric Systems, Machine Learning, Multi-Physical Modeling, RoboticsBrief- MERL researchers presented 13 papers at the recently concluded American Control Conference (ACC) 2025 in Denver, USA. The papers covered a wide range of topics including Bayesian optimization for personalized medicine, machine learning for battery performance in eVTOLs, model predictive control for space and building systems, process systems engineering for sustainability, GNSS-RTK optimization, convex set manipulation, PDE control, servo system modeling, battery fault diagnosis, truck fleet coordination, interactive motion planning, and satellite station keeping. Additionally, MERL researchers (Vedang Deshpande and Ankush Chakrabarty) organized an invited session on design and optimization of energy systems.
As a sponsor of the conference, MERL maintained a booth for open discussions with researchers and students, and hosted a special session to discuss highlights of MERL research and work philosophy.
- MERL researchers presented 13 papers at the recently concluded American Control Conference (ACC) 2025 in Denver, USA. The papers covered a wide range of topics including Bayesian optimization for personalized medicine, machine learning for battery performance in eVTOLs, model predictive control for space and building systems, process systems engineering for sustainability, GNSS-RTK optimization, convex set manipulation, PDE control, servo system modeling, battery fault diagnosis, truck fleet coordination, interactive motion planning, and satellite station keeping. Additionally, MERL researchers (Vedang Deshpande and Ankush Chakrabarty) organized an invited session on design and optimization of energy systems.
-
AWARD Mitsubishi Electric Team Wins Awards at GalFer Contest Date: June 23, 2025
Awarded to: Bingnan Wang, Tatsuya Yamamoto, Yusuke Sakamoto, Siyuan Sun, Toshiaki Koike-Akino, and Ye Wang
MERL Contacts: Toshiaki Koike-Akino; Bingnan Wang; Ye Wang
Research Areas: Machine Learning, Multi-Physical Modeling, OptimizationBrief- The MELSUR (Mitsubishi Electric SURrogate) team, consisting of a group of MERL and Mitsubishi Electric researchers, ranked first in two out of three categories in the GalFer Contest.
The GalFer (Galileo Ferraris) contest aims to compare the accuracy and efficiency of data-driven methodologies for the multi-physics simulation of traction electric machines. A total of 26 teams worldwide participated in the contest, which consists of three categories. The MELSUR team, including MERL staff Bingnan Wang, Toshiaki Koike-Akino, Ye Wang, MERL intern Siyuan Sun, Mitsubishi Electric researchers Tatsuya Yamamoto and Yusuke Sakamoto, ranked first for the category of "Novelty" and "Interpolation". The results were announced during an award ceremony at the COMPUMAG 2025 conference in Naples, Italy.
- The MELSUR (Mitsubishi Electric SURrogate) team, consisting of a group of MERL and Mitsubishi Electric researchers, ranked first in two out of three categories in the GalFer Contest.