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

    • CD1400: 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 with flexible start date after April 1st, 2020.

    • CV1425: Contact implicit manipulation

      The Computer Vision group at MERL is offering and internship opportunity to a highly skilled PhD student to work on robotic manipulation. Candidates should have a solid understanding of contact mechanics, path planning and dexterous manipulation. The intern will deploy control software on physical robots. Strong programming skills are required, including ROS, C++ and Python. Duration and start dates are flexible.

    • CD1401: Formal Synthesis for Planning and Control for Autonomous Systems

      The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to conduct research on planning and control by formal methods, in particular temporal logics specifications and their synthesis by mixed-integer inequalities. The ideal candidate is enrolled in a PhD program in Electrical, Mechanical, Aerospace Engineering, Computer Science or related program, with focus on Control Theory. The ideal candidate will have experience in (one or more of) formal methods, particularly temporal logics and signal temporal logics, reachability analysis, abstractions of dynamical systems, hybrid predictive control, and mixed integer programming. Good programming skills in Matlab (or alternatively Python) are required, working knowledge of C/C++ is a plus. The expected duration of the internship is 3-6 months with flexible start date after April 1st, 2020.


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  • Recent Publications

    •  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 Download PDFAbout TR2020-008
      • @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}
      • }
    •  Muralidharan, V., Weiss, A., Kalabic, U., "Control Strategy for Long-Term Station-Keeping on Near-Rectilinear Halo Orbits", AAS/AIAA Space Flight Mechanics Meeting, DOI: 10.2514/6.2020-1459, January 2020.
      BibTeX Download PDFAbout TR2020-006
      • @inproceedings{Muralidharan2020jan,
      • author = {Muralidharan, Vivek and Weiss, Avishai and Kalabic, Uros},
      • title = {Control Strategy for Long-Term Station-Keeping on Near-Rectilinear Halo Orbits},
      • booktitle = {AAS/AIAA Space Flight Mechanics Meeting},
      • year = 2020,
      • month = jan,
      • doi = {10.2514/6.2020-1459},
      • url = {https://www.merl.com/publications/TR2020-006}
      • }
    •  Jha, D., Kolaric, P., Romeres, D., Raghunathan, A., Benosman, M., Nikovski, D.N., "Robust Optimization for Trajectory-Centric Model-based Reinforcement Learning", NeurIPS Workshop on Safety and Robustness in Decision Making, December 2019.
      BibTeX Download PDFAbout TR2019-156
      • @inproceedings{Jha2019dec2,
      • author = {Jha, Devesh and Kolaric, Patrik and Romeres, Diego and Raghunathan, Arvind and Benosman, Mouhacine and Nikovski, Daniel N.},
      • title = {Robust Optimization for Trajectory-Centric Model-based Reinforcement Learning},
      • booktitle = {NeurIPS Workshop on Safety and Robustness in Decision Making},
      • year = 2019,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2019-156}
      • }
    •  Jha, D., Raghunathan, A., Romeres, D., "QNTRPO: Including Curvature in TRPO", Optimization Foundations for Reinforcement Learning Workshop at NeurIPS, December 2019.
      BibTeX Download PDFAbout TR2019-154
      • @inproceedings{Jha2019dec,
      • author = {Jha, Devesh and Raghunathan, Arvind and Romeres, Diego},
      • title = {QNTRPO: Including Curvature in TRPO},
      • booktitle = {Optimization Foundations for Reinforcement Learning Workshop at NeurIPS},
      • year = 2019,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2019-154}
      • }
    •  Ota, K., Jha, D.K., Oiki, T., Miura, M., Nammoto, T., Nikovski, D., Mariyama, T., "Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), DOI: 10.1109/IROS40897.2019.8968010, ISSN: 2153-0866, ISBN: 978-1-7281-4004-9, November 2019, pp. 3487-3494.
      BibTeX Download PDFAbout TR2019-129
      • @inproceedings{Ota2019nov,
      • author = {Ota, Kei and Jha, Devesh K. and Oiki, Tomohiro and Miura, Mamoru and Nammoto, Takashi and Nikovski, Daniel and Mariyama, Toshisada},
      • title = {Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning},
      • booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
      • year = 2019,
      • pages = {3487--3494},
      • month = nov,
      • publisher = {IEEE},
      • doi = {10.1109/IROS40897.2019.8968010},
      • issn = {2153-0866},
      • isbn = {978-1-7281-4004-9},
      • url = {https://www.merl.com/publications/TR2019-129}
      • }
    •  Jha, D., Raghunathan, A., Romeres, D., "Quasi-Newton Trust Region Policy Optimization", Conference on Robot Learning (CoRL), October 2019.
      BibTeX Download PDFAbout TR2019-120
      • @inproceedings{Jha2019oct,
      • author = {Jha, Devesh and Raghunathan, Arvind and Romeres, Diego},
      • title = {Quasi-Newton Trust Region Policy Optimization},
      • booktitle = {Conference on Robot Learning (CoRL)},
      • year = 2019,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2019-120}
      • }
    •  Almeida, D., Ataer-Cansizoglu, E., Corcodel, R., "Detection, Tracking and 3D Modeling of Objects with Sparse RGB-D SLAM and Interactive Perception", IEEE-RAS International Conference on Humanoid Robots, October 2019.
      BibTeX Download PDFAbout TR2019-119
      • @inproceedings{Almeida2019oct,
      • author = {Almeida, Diogo and Ataer-Cansizoglu, Esra and Corcodel, Radu},
      • title = {Detection, Tracking and 3D Modeling of Objects with Sparse RGB-D SLAM and Interactive Perception},
      • booktitle = {IEEE-RAS International Conference on Humanoid Robots},
      • year = 2019,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2019-119}
      • }
    •  Takahashi, T., Sun, H., Tian, D., Wang, Y., "Learning Heuristic Functions for Mobile Robot Path Planning Using Deep Neural Networks", International Conference on Automated Planning and Scheduling (ICAPS), July 2019, pp. 764-772.
      BibTeX Download PDFAbout TR2019-072
      • @inproceedings{Takahashi2019jul,
      • author = {Takahashi, Takeshi and Sun, He and Tian, Dong and Wang, Yebin},
      • title = {Learning Heuristic Functions for Mobile Robot Path Planning Using Deep Neural Networks},
      • booktitle = {International Conference on Automated Planning and Scheduling (ICAPS)},
      • year = 2019,
      • pages = {764--772},
      • month = jul,
      • publisher = {AAAI},
      • url = {https://www.merl.com/publications/TR2019-072}
      • }
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