MERL - Mitsubishi Electric Research Laboratories

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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.

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  • Awards

    •  AWARD   MERL Researcher Devesh Jha Wins the Rudolf Kalman Best Paper Award 2019
      Date: October 10, 2019
      Awarded to: Devesh Jha, Nurali Virani, Zhenyuan Yuan, Ishana Shekhawat and Asok Ray
      MERL Contact: Devesh Jha
      Research Areas: Artificial Intelligence, Control, Data Analytics, Machine Learning, Robotics
      Brief
      • MERL researcher Devesh Jha has won the Rudolf Kalman Best Paper Award 2019 for the paper entitled "Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models". This paper, published in a Special Commemorative Issue for Rudolf E. Kalman in the ASME JDSMC in March 2018, uses Bayesian filtering for imitation learning in Hidden Mode Hybrid Systems. This award is given annually by the Dynamic Systems and Control Division of ASME to the authors of the best paper published in the ASME Journal of Dynamic Systems Measurement and Control during the preceding year.
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  • News & Events


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  • Internships

    • CA1400: Autonomous Vehicle Planning and Control

      The Control and Dynamical Systems (CD) group at MERL is seeking highly motivated interns at different levels of expertise to conduct research on planning and control for autonomous vehicles. The research domain includes algorithms for path planning, vehicle control, high level decision making, sensor-based navigation, driver-vehicle interaction. PhD students will be considered for algorithm development and analysis, and property proving. Master students will be considered for development and implementation in a scaled robotic test bench for autonomous vehicles. For algorithm development and analysis it is highly desirable to have deep background in one or more among: sampling-based planning methods, particle filtering, model predictive control, reachability methods, formal methods and abstractions of dynamical systems, and experience with their implementation in Matlab/Python/C++. For algorithm implementation, it is required to have working knowledge of Matlab, C++, and ROS, and it is a plus to have background in some of the above mentioned methods. The expected duration of the internship is 3-6 months.


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  • Recent Publications

    •  Romeres, D., Liu, Y., Jha, D., Nikovski, D.N., "Understanding Multi-Modal Perception Using Behavioral Cloning for Peg-In-a-Hole Insertion Tasks", Robotics: Science and Systems, July 2020.
      BibTeX TR2020-110 PDF
      • @inproceedings{Romeres2020jul,
      • author = {Romeres, Diego and Liu, Yifang and Jha, Devesh and Nikovski, Daniel N.},
      • title = {Understanding Multi-Modal Perception Using Behavioral Cloning for Peg-In-a-Hole Insertion Tasks},
      • booktitle = {Robotics: Science and Systems},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-110}
      • }
    •  Ota, K., Oiki, T., Jha, D., Mariyama, T., Nikovski, D.N., "Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?", International Conference on Machine Learning (ICML), June 2020.
      BibTeX TR2020-083 PDF Software
      • @inproceedings{Ota2020jun,
      • author = {Ota, Kei and Oiki, Tomoaki and Jha, Devesh and Mariyama, Toshisada and Nikovski, Daniel N.},
      • title = {Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?},
      • booktitle = {International Conference on Machine Learning (ICML)},
      • year = 2020,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2020-083}
      • }
    •  Jha, D., Kolaric, P., Raghunathan, A., Lewis, F., Benosman, M., Romeres, D., Nikovski, D.N., "Local Policy Optimization for Trajectory-Centric Reinforcement Learning", IEEE International Conference on Robotics and Automation (ICRA), Ayanna Howard, Eds., May 2020, pp. 5094-5100.
      BibTeX TR2020-062 PDF
      • @inproceedings{Jha2020may,
      • author = {Jha, Devesh and Kolaric, Patrik and Raghunathan, Arvind and Lewis, Frank and Benosman, Mouhacine and Romeres, Diego and Nikovski, Daniel N.},
      • title = {Local Policy Optimization for Trajectory-Centric Reinforcement Learning},
      • booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
      • year = 2020,
      • editor = {Ayanna Howard},
      • pages = {5094--5100},
      • month = may,
      • publisher = {IEEE},
      • isbn = {978-1-7281-7395-5},
      • url = {https://www.merl.com/publications/TR2020-062}
      • }
    •  Onol, A.O., Corcodel, R., Long, P., Padir, T., "Tuning-Free Contact-Implicit Trajectory Optimization", IEEE International Conference on Robotics and Automation (ICRA), May 2020.
      BibTeX TR2020-065 PDF Video Software
      • @inproceedings{Onol2020may,
      • author = {Onol, Aykut O. and Corcodel, Radu and Long, Philip and Padir, Taskin},
      • title = {Tuning-Free Contact-Implicit Trajectory Optimization},
      • booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
      • year = 2020,
      • month = may,
      • url = {https://www.merl.com/publications/TR2020-065}
      • }
    •  Romeres, D., Dalla Libera, A., Jha, D., Yerazunis, W.S., Nikovski, D.N., "Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements", Robotics and Automation Letters, DOI: 10.1109/LRA.2020.2977255, Vol. 5, No. 2, pp. 3548-3555, May 2020.
      BibTeX TR2020-063 PDF
      • @article{Romeres2020may,
      • author = {Romeres, Diego and Dalla Libera, Alberto and Jha, Devesh and Yerazunis, William S. and Nikovski, Daniel N.},
      • title = {Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements},
      • journal = {Robotics and Automation Letters},
      • year = 2020,
      • volume = 5,
      • number = 2,
      • pages = {3548--3555},
      • month = may,
      • doi = {10.1109/LRA.2020.2977255},
      • issn = {2377-3766},
      • url = {https://www.merl.com/publications/TR2020-063}
      • }
    •  Bortoff, S.A., "Modeling Contact and Collisions for Robotic Assembly Control", American Modelica Conference 2020, March 2020.
      BibTeX TR2020-032 PDF
      • @inproceedings{Bortoff2020mar,
      • author = {Bortoff, Scott A.},
      • title = {Modeling Contact and Collisions for Robotic Assembly Control},
      • booktitle = {American Modelica Conference 2020},
      • year = 2020,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2020-032}
      • }
    •  Romeres, D., Dalla Libera, A., Jha, D., Yerazunis, W.S., Nikovski, D.N., "Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements", arXiv, DOI: 10.1109/LRA.2020.2977255, February 2020.
      BibTeX arXiv
      • @article{Romeres2020feb,
      • author = {Romeres, Diego and Dalla Libera, Alberto and Jha, Devesh and Yerazunis, William S. and Nikovski, Daniel N.},
      • title = {Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements},
      • journal = {arXiv},
      • year = 2020,
      • month = feb,
      • doi = {10.1109/LRA.2020.2977255},
      • issn = {2377-3766},
      • url = {https://arxiv.org/abs/2002.10621}
      • }
    •  Caverly, R., Di Cairano, S., Weiss, A., "Control Allocation and Quantization of a GEO Satellite with 4DOF Gimbaled Thruster Booms", AAS/AIAA Space Flight Mechanics Meeting, DOI: 10.2514/6.2020-1687, January 2020.
      BibTeX TR2020-008 PDF
      • @inproceedings{Caverly2020jan,
      • author = {Caverly, Ryan and Di Cairano, Stefano and Weiss, Avishai},
      • title = {Control Allocation and Quantization of a GEO Satellite with 4DOF Gimbaled Thruster Booms},
      • booktitle = {AAS/AIAA Space Flight Mechanics Meeting},
      • year = 2020,
      • month = jan,
      • doi = {10.2514/6.2020-1687},
      • url = {https://www.merl.com/publications/TR2020-008}
      • }
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