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.

  • 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.
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  • News & Events


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  • Internships

    • CA1531: Learning-based multi-agent motion planning

      MERL is seeking a highly motivated intern to research multi-agent motion planning by combining optimization-based methods with machine learning. The ideal candidate is enrolled in a PhD program in Electrical, Mechanical, Aerospace Engineering, Robotics, Computer Science or related program, with prior experience in multi-agent motion planning, machine learning (especially supervised, reinforcement, and safe ML), and convex and non-convex optimization. A successful internship will result in innovative methods for multiagent planning, in the development of well-documented (Python/MATLAB) code for validating the proposed methods, and in the submission of relevant results for publication in peer-reviewed conference proceedings and journals. The expected duration of the internship is 3 months with a flexible start date in the Spring/Summer 2021. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • CV1541: Computer Vision for Robotic Manipulation

      MERL is looking for a highly motivated and qualified intern to work on computer vision for robotic manipulation. The ideal candidate would be a current Ph.D. student with a strong background in computer vision, deep learning, and/or robotics. There are several available topics for consideration including learning for object manipulation, grasp detection and regrasping, pose estimation, and intent recognition for human-robot interaction. The internship requires development of novel algorithms which can be implemented and evaluated on a robotic test-bed. Experience in working with a physics engine simulator like Mujoco, pyBullet, or Gazebo is required. Proficiency in Python programming is necessary and experience with ROS is a plus. Successful candidate will collaborate with MERL researchers and publication of the relevant results is expected. Start date is flexible and expected duration of the internship is 3-4 months. Interested candidates are encouraged to apply with their recent CV and list of publications in related topics. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • CA1521: Coordinated Perception and Control for Autonomous Systems

      MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in the development of algorithms for coordinating control and perception in autonomous systems. The overall objective is to determine the sensing strategy together with the motion/control strategy to effectively achieve a control goal while managing the risk due to the environment uncertainty. The ideal candidate is expected to be working towards a PhD with strong emphasis in control or planning algorithms, and to have interest and background in as many as possible among: predictive control, stochastic tubes, scenario-based stochastic optimization, uncertainty and risk representation, machine learning and motion planning algorithms. Good programming skills in MATLAB and/or Python, are required. The expected duration of the internship is in the Spring of 2021, for a duration of 3-6 months. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.


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  • Recent Publications

    •  Romeres, D., Amadio, F., Dalla Libera, A., Nikovski, D.N., Carli, R., "Model-based Policy Search for Partially Measurable Systems", Advances in Neural Information Processing Systems (NeurIPS), December 2020.
      BibTeX TR2020-174 PDF
      • @inproceedings{Romeres2020dec2,
      • author = {Romeres, Diego and Amadio, Fabio and Dalla Libera, Alberto and Nikovski, Daniel N. and Carli, Ruggero},
      • title = {Model-based Policy Search for Partially Measurable Systems},
      • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
      • year = 2020,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2020-174}
      • }
    •  Romeres, D., Dalla Libera, A., Amadio, F., Carli, R., "Feedback Linearization Robot Control based on Gaussian Process Inverse Dynamics Model", Conferenza Italiana di Robotica e Macchine Intelligenti, December 2020.
      BibTeX TR2020-173 PDF
      • @inproceedings{Romeres2020dec,
      • author = {Romeres, Diego and Dalla Libera, Alberto and Amadio, Fabio and Carli, Ruggero},
      • title = {Feedback Linearization Robot Control based on Gaussian Process Inverse Dynamics Model},
      • booktitle = {Conferenza Italiana di Robotica e Macchine Intelligenti},
      • year = 2020,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2020-173}
      • }
    •  Bortoff, S.A., Okasha, A., "Modelica-Based Control of A Delta Robot", ASME Dynamic Systems and Control Conference, December 2020.
      BibTeX TR2020-154 PDF
      • @inproceedings{Bortoff2020dec,
      • author = {Bortoff, Scott A. and Okasha, Ahmed},
      • title = {Modelica-Based Control of A Delta Robot},
      • booktitle = {ASME Dynamic Systems and Control Conference},
      • year = 2020,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2020-154}
      • }
    •  Ahn, H., Berntorp, K., Di Cairano, S., "Cooperating Modular Goal Selection and Motion Planning for Autonomous Driving", IEEE Conference on Decision and Control (CDC), December 2020.
      BibTeX TR2020-168 PDF
      • @inproceedings{Ahn2020dec2,
      • author = {Ahn, Heejin and Berntorp, Karl and Di Cairano, Stefano},
      • title = {Cooperating Modular Goal Selection and Motion Planning for Autonomous Driving},
      • booktitle = {IEEE Conference on Decision and Control (CDC)},
      • year = 2020,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2020-168}
      • }
    •  Ahn, H., Berntorp, K., Inani, P., Ram, A.J., Di Cairano, S., "Reachability-based Decision Making for Autonomous Driving: Theory and Experiment", IEEE Transactions on Control Systems Technology, December 2020.
      BibTeX TR2020-165 PDF
      • @article{Ahn2020dec,
      • author = {Ahn, Heejin and Berntorp, Karl and Inani, Pranav and Ram, Arjun Jagdish and Di Cairano, Stefano},
      • title = {Reachability-based Decision Making for Autonomous Driving: Theory and Experiment},
      • journal = {IEEE Transactions on Control Systems Technology},
      • year = 2020,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2020-165}
      • }
    •  Corcodel, R., Jain, S., van Baar, J., "Interactive Tactile Perception for Classification of Novel Object Instances", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 2020.
      BibTeX TR2020-143 PDF
      • @inproceedings{Corcodel2020nov,
      • author = {Corcodel, Radu and Jain, Siddarth and van Baar, Jeroen},
      • title = {Interactive Tactile Perception for Classification of Novel Object Instances},
      • booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-143}
      • }
    •  Ota, K., Jha, D., Onishi, T., Kanezaki, A., Yoshiyasu, Y., Mariyama, T., Nikovski, D.N., "Deep Reactive Planning in Dynamic Environments", Conference on Robot Learning (CoRL), November 2020.
      BibTeX TR2020-144 PDF
      • @inproceedings{Ota2020nov2,
      • author = {Ota, Kei and Jha, Devesh and Onishi, Tadashi and Kanezaki, Asako and Yoshiyasu, Yusuke and Mariyama, Toshisada and Nikovski, Daniel N.},
      • title = {Deep Reactive Planning in Dynamic Environments},
      • booktitle = {Conference on Robot Learning (CoRL)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-144}
      • }
    •  Zhang, W., Seto, S., Jha, D., "CAZSL: Zero-Shot Regression for Pushing Models by Generalizing Through Context", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 2020.
      BibTeX TR2020-140 PDF Software
      • @inproceedings{Zhang2020nov,
      • author = {Zhang, Wenyu and Seto, Skyler and Jha, Devesh},
      • title = {CAZSL: Zero-Shot Regression for Pushing Models by Generalizing Through Context},
      • booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-140}
      • }
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