Robotics
Where hardware, software and machine intelligence come together.
Our research is interdisciplinary and focuses on sensing, planning, reasoning, and control of single and multi-agent systems, including both manipulation and mobile robots. We strive to develop algorithms and methods for factory automation, smart building and transportation applications using machine learning, computer vision, RF/optical sensing, wireless communications, control theory and signal processing. Key research themes include bin picking and object manipulation, sensing and mapping of indoor areas, coordinated control of robot swarms, as well as robot learning and simulation.
Quick Links
-
Researchers
Devesh K.
Jha
Diego
Romeres
Daniel N.
Nikovski
Arvind
Raghunathan
Stefano
Di Cairano
Siddarth
Jain
William S.
Yerazunis
Radu
Corcodel
Yebin
Wang
Toshiaki
Koike-Akino
Yuki
Shirai
Abraham P.
Vinod
Tim K.
Marks
Avishai
Weiss
Scott A.
Bortoff
Chiori
Hori
Ye
Wang
Anoop
Cherian
Jonathan
Le Roux
Matthew
Brand
Philip V.
Orlik
Alexander
Schperberg
Bingnan
Wang
Abraham
Goldsmith
Jianlin
Guo
Sameer
Khurana
Jing
Liu
Hassan
Mansour
Pedro
Miraldo
Saviz
Mowlavi
Anthony
Vetro
James
Queeney
-
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, RoboticsBrief- 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.
- 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.
-
AWARD Honorable Mention Award at NeurIPS 23 Instruction Workshop Date: December 15, 2023
Awarded to: Lingfeng Sun, Devesh K. Jha, Chiori Hori, Siddharth Jain, Radu Corcodel, Xinghao Zhu, Masayoshi Tomizuka and Diego Romeres
MERL Contacts: Radu Corcodel; Chiori Hori; Siddarth Jain; Devesh K. Jha; Diego Romeres
Research Areas: Artificial Intelligence, Machine Learning, RoboticsBrief- MERL Researchers received an "Honorable Mention award" at the Workshop on Instruction Tuning and Instruction Following at the NeurIPS 2023 conference in New Orleans. The workshop was on the topic of instruction tuning and Instruction following for Large Language Models (LLMs). MERL researchers presented their work on interactive planning using LLMs for partially observable robotic tasks during the oral presentation session at the workshop.
-
AWARD Joint University of Padua-MERL team wins Challenge 'AI Olympics With RealAIGym' Date: August 25, 2023
Awarded to: Alberto Dalla Libera, Niccolo' Turcato, Giulio Giacomuzzo, Ruggero Carli, Diego Romeres
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Machine Learning, RoboticsBrief- A joint team consisting of members of University of Padua and MERL ranked 1st in the IJCAI2023 Challenge "Al Olympics With RealAlGym: Is Al Ready for Athletic Intelligence in the Real World?". The team was composed by MERL researcher Diego Romeres and a team from University Padua (UniPD) consisting of Alberto Dalla Libera, Ph.D., Ph.D. Candidates: Niccolò Turcato, Giulio Giacomuzzo and Prof. Ruggero Carli from University of Padua.
The International Joint Conference on Artificial Intelligence (IJCAI) is a premier gathering for AI researchers and organizes several competitions. This year the competition CC7 "AI Olympics With RealAIGym: Is AI Ready for Athletic Intelligence in the Real World?" consisted of two stages: simulation and real-robot experiments on two under-actuated robotic systems. The two robotics systems were treated as separate tracks and one final winner was selected for each track based on specific performance criteria in the control tasks.
The UniPD-MERL team competed and won in both tracks. The team's system made strong use of a Model-based Reinforcement Learning algorithm called (MC-PILCO) that we recently published in the journal IEEE Transaction on Robotics.
- A joint team consisting of members of University of Padua and MERL ranked 1st in the IJCAI2023 Challenge "Al Olympics With RealAlGym: Is Al Ready for Athletic Intelligence in the Real World?". The team was composed by MERL researcher Diego Romeres and a team from University Padua (UniPD) consisting of Alberto Dalla Libera, Ph.D., Ph.D. Candidates: Niccolò Turcato, Giulio Giacomuzzo and Prof. Ruggero Carli from University of Padua.
See All Awards for Robotics -
-
News & Events
-
NEWS MERL Researchers to Present 2 Conference and 11 Workshop Papers at NeurIPS 2024 Date: December 10, 2024 - December 15, 2024
Where: Advances in Neural Processing Systems (NeurIPS)
MERL Contacts: Petros T. Boufounos; Matthew Brand; Ankush Chakrabarty; Anoop Cherian; François Germain; Toshiaki Koike-Akino; Christopher R. Laughman; Jonathan Le Roux; Jing Liu; Suhas Lohit; Tim K. Marks; Yoshiki Masuyama; Kieran Parsons; Kuan-Chuan Peng; Diego Romeres; Pu (Perry) Wang; Ye Wang; Gordon Wichern
Research Areas: Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Human-Computer Interaction, Information SecurityBrief- MERL researchers will attend and present the following papers at the 2024 Advances in Neural Processing Systems (NeurIPS) Conference and Workshops.
1. "RETR: Multi-View Radar Detection Transformer for Indoor Perception" by Ryoma Yataka (Mitsubishi Electric), Adriano Cardace (Bologna University), Perry Wang (Mitsubishi Electric Research Laboratories), Petros Boufounos (Mitsubishi Electric Research Laboratories), Ryuhei Takahashi (Mitsubishi Electric). Main Conference. https://neurips.cc/virtual/2024/poster/95530
2. "Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads" by Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Joanna Matthiesen (Math Kangaroo USA), Kevin Smith (Massachusetts Institute of Technology), Josh Tenenbaum (Massachusetts Institute of Technology). Main Conference, Datasets and Benchmarks track. https://neurips.cc/virtual/2024/poster/97639
3. "Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?" by Young-Jin Park (Massachusetts Institute of Technology), Jing Liu (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Gordon Wichern (Mitsubishi Electric Research Laboratories), Navid Azizan (Massachusetts Institute of Technology), Christopher R. Laughman (Mitsubishi Electric Research Laboratories), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories). Time Series in the Age of Large Models Workshop.
4. "Forget to Flourish: Leveraging Model-Unlearning on Pretrained Language Models for Privacy Leakage" by Md Rafi Ur Rashid (Penn State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Shagufta Mehnaz (Penn State University), Ye Wang (Mitsubishi Electric Research Laboratories). Workshop on Red Teaming GenAI: What Can We Learn from Adversaries?
5. "Spatially-Aware Losses for Enhanced Neural Acoustic Fields" by Christopher Ick (New York University), Gordon Wichern (Mitsubishi Electric Research Laboratories), Yoshiki Masuyama (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Jonathan Le Roux (Mitsubishi Electric Research Laboratories). Audio Imagination Workshop.
6. "FV-NeRV: Neural Compression for Free Viewpoint Videos" by Sorachi Kato (Osaka University), Takuya Fujihashi (Osaka University), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Takashi Watanabe (Osaka University). Machine Learning and Compression Workshop.
7. "GPT Sonography: Hand Gesture Decoding from Forearm Ultrasound Images via VLM" by Keshav Bimbraw (Worcester Polytechnic Institute), Ye Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). AIM-FM: Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond Workshop.
8. "Smoothed Embeddings for Robust Language Models" by Hase Ryo (Mitsubishi Electric), Md Rafi Ur Rashid (Penn State University), Ashley Lewis (Ohio State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kieran Parsons (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories). Safe Generative AI Workshop.
9. "Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation" by Xiangyu Chen (University of Kansas), Ye Wang (Mitsubishi Electric Research Laboratories), Matthew Brand (Mitsubishi Electric Research Laboratories), Pu Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). Workshop on Adaptive Foundation Models.
10. "Preference-based Multi-Objective Bayesian Optimization with Gradients" by Joshua Hang Sai Ip (University of California Berkeley), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Ali Mesbah (University of California Berkeley), Diego Romeres (Mitsubishi Electric Research Laboratories). Workshop on Bayesian Decision-Making and Uncertainty. Lightning talk spotlight.
11. "TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensions with Trust-Region-based Bayesian Novelty Search" by Wei-Ting Tang (Ohio State University), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Joel A. Paulson (Ohio State University). Workshop on Bayesian Decision-Making and Uncertainty.
12. "MEL-PETs Joint-Context Attack for the NeurIPS 2024 LLM Privacy Challenge Red Team Track" by Ye Wang (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Special Award for Practical Attack.
13. "MEL-PETs Defense for the NeurIPS 2024 LLM Privacy Challenge Blue Team Track" by Jing Liu (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Won 3rd Place Award.
MERL members also contributed to the organization of the Multimodal Algorithmic Reasoning (MAR) Workshop (https://marworkshop.github.io/neurips24/). Organizers: Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Honglu Zhou (Salesforce Research), Kevin Smith (Massachusetts Institute of Technology), Tim K. Marks (Mitsubishi Electric Research Laboratories), Juan Carlos Niebles (Salesforce AI Research), Petar Veličković (Google DeepMind).
- MERL researchers will attend and present the following papers at the 2024 Advances in Neural Processing Systems (NeurIPS) Conference and Workshops.
-
NEWS MERL Researcher Supports Festival of Italian Creativity by introducing Robotics to Middle Schoolers Date: November 14, 2024 - November 22, 2024
Where: Italian Consulate
MERL Contact: Diego Romeres
Research Area: RoboticsBrief- Prof. Zunino from the University of Genoa, with support from MERL Researcher Diego Romeres, organized a robotic workshop that introduced 6th-8th grade students from the greater Boston area to the fundamentals of robotics. The workshop provided students with hands-on experience in robotic technology using LEGO systems. Participants learned key principles of robotics, teamwork, and project planning. They worked collaboratively to design, program using visual-based software, and solve challenges as field engineers.
The workshop event was part of the Festival of Italian Creativity organized by the Italian consulate to honor the naming of Boston as a Capital of Italian Creativity.
- Prof. Zunino from the University of Genoa, with support from MERL Researcher Diego Romeres, organized a robotic workshop that introduced 6th-8th grade students from the greater Boston area to the fundamentals of robotics. The workshop provided students with hands-on experience in robotic technology using LEGO systems. Participants learned key principles of robotics, teamwork, and project planning. They worked collaboratively to design, program using visual-based software, and solve challenges as field engineers.
See All News & Events for Robotics -
-
Internships
-
CA0107: Internship - Perception-Aware Control and Planning
MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in the development of visual perception-aware control. The overall objective is to optimize control policy where the perception uncertainty is affected by the chosen policy. Application areas include mobile robotics, drones, autonomous vehicles, and spacecraft. The ideal candidate is expected to be working towards a PhD with a strong emphasis on stochastic optimal control/planning or visual odometry and to have interest and background in as many as possible among: output-feedback optimal control, visual SLAM, POMDP, information fields, motion planning, and machine learning. The expected start date is in the late Spring/Early Summer 2025, for a duration of 3-6 months.
Required Specific Experience
- Current/Past enrollment in a PhD program in Mechanical, Aerospace, Electrical Engineering, or a related field
- 2+ years of research in at least some of: optimal control, motion planning, computer vision, navigation, uncertainty quantification, stochastic planning/control
- Strong programming skills in Python and/or C++
-
CV0075: Internship - Multimodal Embodied AI
MERL is looking for a self-motivated intern to work on problems at the intersection of multimodal large language models and embodied AI in dynamic indoor environments. The ideal candidate would be a PhD student with a strong background in machine learning and computer vision, as demonstrated by top-tier publications. The candidate must have prior experience in designing synthetic scenes (e.g., 3D games) using popular graphics software, embodied AI, large language models, reinforcement learning, and the use of simulators such as Habitat/SoundSpaces. Hands on experience in using animated 3D human shape models (e.g., SMPL and variants) is desired. The intern is expected to collaborate with researchers in computer vision at MERL to develop algorithms and prepare manuscripts for scientific publications.
Required Specific Experience
- Experience in designing 3D interactive scenes
- Experience with vision based embodied AI using simulators (implementation on real robotic hardware would be a plus).
- Experience training large language models on multimodal data
- Experience with training reinforcement learning algorithms
- Strong foundations in machine learning and programming
- Strong track record of publications in top-tier computer vision and machine learning venues (such as CVPR, NeurIPS, etc.).
-
CA0117: Internship - Feedforward-Feedback Co-Design
MERL is seeking a graduate student to develop a scalable optimization-based framework for feedforward-feedback co-design for nonlinear dynamical systems subject to path constraints. The framework will 1) support modeling and operational uncertainties, and 2) guarantee static and dynamic feasibility in closed-loop. The solution approach will leverage the state-of-the-art in sequential convex programming, contraction analysis, and first-order methods for semidefinite programming. The methods will be evaluated on high-dimensional motion planning problems in robotics. The results of the internship are expected to be published in top-tier conferences and/or journal in robotics, control systems, and optimization.
The internship is expected to start in Spring or Summer 2025 with an expected duration of 3-6 months depending on the agreed scope and intermediate progress.
Required Specific Experience
- Current/Past enrollment in a Ph.D. program in Mechanical, Aerospace, Electrical Engineering, Computer Science, or Applied Mathematics.
- 2+ years of research in at least some of: first-order algorithms for SDPs, contraction analysis, nonconvex trajectory optimization.
- Strong programming skills in Python and/or C/C++.
See All Internships for Robotics -
-
Openings
-
CV0124: Postdoctoral Research Fellow - 3D Computer Vision
-
CA0093: Research Scientist - Control for Autonomous Systems
See All Openings at MERL -
-
Recent Publications
- "Chance-Constrained Optimization for Contact-rich Systems using Mixed Integer Programming", Nonlinear Analysis: Hybrid Systems, DOI: 10.1016/j.nahs.2024.101466, Vol. 52, December 2024.BibTeX TR2024-008 PDF
- @article{Shirai2024dec,
- author = {Shirai, Yuki and Jha, Devesh K. and Raghunathan, Arvind and Romeres, Diego},
- title = {Chance-Constrained Optimization for Contact-rich Systems using Mixed Integer Programming},
- journal = {Nonlinear Analysis: Hybrid Systems},
- year = 2024,
- volume = 52,
- month = dec,
- doi = {10.1016/j.nahs.2024.101466},
- issn = {1751-570X},
- url = {https://www.merl.com/publications/TR2024-008}
- }
, - "Memory-Based Learning of Global Control Policies from Local Controllers", 21st International Conference on Informatics in Control, Automation and Robotics (ICINCO'24), November 2024.BibTeX TR2024-158 PDF
- @inproceedings{Nikovski2024nov,
- author = {{Nikovski, Daniel N. and Zhong, Junmin and Yerazunis, William S.}},
- title = {Memory-Based Learning of Global Control Policies from Local Controllers},
- booktitle = {21st International Conference on Informatics in Control, Automation and Robotics (ICINCO'24)},
- year = 2024,
- month = nov,
- url = {https://www.merl.com/publications/TR2024-158}
- }
, - "Open Human-Robot Collaboration Systems (OHRCS): A Research Perspective", IEEE International Conference on Cognitive Machine Intelligence (CogML 2024), October 2024.BibTeX TR2024-150 PDF
- @inproceedings{Suresh2024nov,
- author = {{Suresh, Prasanth and Romeres, Diego and Dosh, Prashant and Jain, Siddarth}},
- title = {Open Human-Robot Collaboration Systems (OHRCS): A Research Perspective},
- booktitle = {IEEE International Conference on Cognitive Machine Intelligence (CogML 2024)},
- year = 2024,
- month = oct,
- url = {https://www.merl.com/publications/TR2024-150}
- }
, - "Insert-One: One-Shot Robust Visual-Force Servoing for Novel Object Insertion with 6-DoF Tracking", 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024), October 2024.BibTeX TR2024-137 PDF
- @inproceedings{Chang2024oct,
- author = {Chang, Haonan and Boularias, Abdeslam and Jain, Siddarth}},
- title = {Insert-One: One-Shot Robust Visual-Force Servoing for Novel Object Insertion with 6-DoF Tracking},
- booktitle = {2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)},
- year = 2024,
- month = oct,
- url = {https://www.merl.com/publications/TR2024-137}
- }
, - "Learning control of underactuated double pendulum with Model-Based Reinforcement Learning", Competition: AI Olympics With RealAIGym, October 2024.BibTeX TR2024-142 PDF
- @inproceedings{DallaLibera2024oct,
- author = {Dalla Libera, Alberto and Turcato, Niccolò and Giacomuzzo, Giulio and Carli, Ruggero and Romeres, Diego}},
- title = {Learning control of underactuated double pendulum with Model-Based Reinforcement Learning},
- booktitle = {Competition: AI Olympics With RealAIGym},
- year = 2024,
- month = oct,
- url = {https://www.merl.com/publications/TR2024-142}
- }
, - "DECAF: a Discrete-Event based Collaborative Human-Robot Framework for Furniture Assembly", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2024.BibTeX TR2024-138 PDF
- @inproceedings{Giacomuzzo2024oct,
- author = {Giacomuzzo, Giulio and Terreran, Matteo and Jain, Siddarth and Romeres, Diego}},
- title = {DECAF: a Discrete-Event based Collaborative Human-Robot Framework for Furniture Assembly},
- booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
- year = 2024,
- month = oct,
- url = {https://www.merl.com/publications/TR2024-138}
- }
, - "Open Human-Robot Collaboration using Decentralized Inverse Reinforcement Learning", 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024), October 2024.BibTeX TR2024-135 PDF
- @inproceedings{Suresh2024oct,
- author = {Suresh, Prasanth and Jain, Siddarth and Doshi, Prashant and Romeres, Diego}},
- title = {Open Human-Robot Collaboration using Decentralized Inverse Reinforcement Learning},
- booktitle = {2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)},
- year = 2024,
- month = oct,
- url = {https://www.merl.com/publications/TR2024-135}
- }
, - "Autonomous Robotic Assembly: From Part Singulation to Precise Assembly", IEEE/RSJ International Conference on Intelligent Robots and Systems., October 2024.BibTeX TR2024-133 PDF
- @inproceedings{Ota2024oct,
- author = {Ota, Kei and Jha, Devesh K. and Jain, Siddarth and Yerazunis, William S. and Corcodel, Radu and Shukla, Yash and Bronars, Antonia and Romeres, Diego}},
- title = {Autonomous Robotic Assembly: From Part Singulation to Precise Assembly},
- booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems.},
- year = 2024,
- month = oct,
- url = {https://www.merl.com/publications/TR2024-133}
- }
,
- "Chance-Constrained Optimization for Contact-rich Systems using Mixed Integer Programming", Nonlinear Analysis: Hybrid Systems, DOI: 10.1016/j.nahs.2024.101466, Vol. 52, December 2024.
-
Videos
-
Software & Data Downloads
-
Lagrangian Inspired Polynomial for Robot Inverse Dynamics -
Monte Carlo Probabilistic Inference for Learning COntrol -
Python-based Robotic Control & Optimization Package -
Context-Aware Zero Shot Learning -
Online Feature Extractor Network -
Quasi-Newton Trust Region Policy Optimization -
Circular Maze Environment
-