MERL - Mitsubishi Electric Research Laboratories

Share:

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

  • 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.
    •  

    See All Awards for MERL

  • News & Events


    See All News & Events for Robotics

  • Internships

    • CA1401: 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.

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


    See All Internships for Robotics

  • Recent Publications

    •  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 = {http://arxiv.org/pdf/2002.10621.pdf}
      • }
    •  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}
      • }
    •  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 TR2020-006 PDF
      • @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 TR2019-156 PDF
      • @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 TR2019-154 PDF Software
      • @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, November 2019, pp. 3487-3494.
      BibTeX TR2019-129 PDF
      • @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 TR2019-120 PDF Software
      • @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}
      • }
    See All Publications for Robotics

  • Videos

  • Software Downloads