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
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Researchers
Devesh K.
Jha
Daniel N.
Nikovski
Diego
Romeres
Mouhacine
Benosman
Stefano
Di Cairano
Arvind
Raghunathan
Yebin
Wang
Toshiaki
Koike-Akino
William S.
Yerazunis
Karl
Berntorp
Scott A.
Bortoff
Radu
Corcodel
Tim K.
Marks
Matthew E.
Brand
Siddarth
Jain
Alan
Sullivan
Bingnan
Wang
Ye
Wang
Avishai
Weiss
Jianlin
Guo
Jonathan
Le Roux
Philip V.
Orlik
Ronald N.
Perry
Rien
Quirynen
Koon Hoo
Teo
Varun
Haritsa
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Awards
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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 K. Jha
Research Areas: Artificial Intelligence, Control, Data Analytics, Machine Learning, RoboticsBrief- 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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EVENT Prof. Melanie Zeilinger of ETH to give keynote at MERL's Virtual Open House Date & Time: Thursday, December 9, 2021; 1:00pm - 5:30pm EST
Speaker: Prof. Melanie Zeilinger, ETH
Location: Virtual Event
Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Digital Video, Human-Computer Interaction, Information SecurityBrief- MERL is excited to announce the second keynote speaker for our Virtual Open House 2021:
Prof. Melanie Zeilinger from ETH .
Our virtual open house will take place on December 9, 2021, 1:00pm - 5:30pm (EST).
Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities. Prof. Zeilinger's talk is scheduled for 3:15pm - 3:45pm (EST).
Registration: https://mailchi.mp/merl/merlvoh2021
Keynote Title: Control Meets Learning - On Performance, Safety and User Interaction
Abstract: With increasing sensing and communication capabilities, physical systems today are becoming one of the largest generators of data, making learning a central component of autonomous control systems. While this paradigm shift offers tremendous opportunities to address new levels of system complexity, variability and user interaction, it also raises fundamental questions of learning in a closed-loop dynamical control system. In this talk, I will present some of our recent results showing how even safety-critical systems can leverage the potential of data. I will first briefly present concepts for using learning for automatic controller design and for a new safety framework that can equip any learning-based controller with safety guarantees. The second part will then discuss how expert and user information can be utilized to optimize system performance, where I will particularly highlight an approach developed together with MERL for personalizing the motion planning in autonomous driving to the individual driving style of a passenger.
- MERL is excited to announce the second keynote speaker for our Virtual Open House 2021:
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EVENT Prof. Ashok Veeraraghavan of Rice University to give keynote at MERL's Virtual Open House Date & Time: Thursday, December 9, 2021; 1:00pm - 5:30pm EST
Speaker: Prof. Ashok Veeraraghavan, Rice University
Location: Virtual Event
Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Digital Video, Human-Computer Interaction, Information SecurityBrief- MERL is excited to announce the first keynote speaker for our Virtual Open House 2021:
Prof. Ashok Veeraraghavan from Rice University.
Our virtual open house will take place on December 9, 2021, 1:00pm - 5:30pm (EST).
Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities. Prof. Veeraraghavan's talk is scheduled for 1:15pm - 1:45pm (EST).
Registration: https://mailchi.mp/merl/merlvoh2021
Keynote Title: Computational Imaging: Beyond the limits imposed by lenses.
Abstract: The lens has long been a central element of cameras, since its early use in the mid-nineteenth century by Niepce, Talbot, and Daguerre. The role of the lens, from the Daguerrotype to modern digital cameras, is to refract light to achieve a one-to-one mapping between a point in the scene and a point on the sensor. This effect enables the sensor to compute a particular two-dimensional (2D) integral of the incident 4D light-field. We propose a radical departure from this practice and the many limitations it imposes. In the talk we focus on two inter-related research projects that attempt to go beyond lens-based imaging.
First, we discuss our lab’s recent efforts to build flat, extremely thin imaging devices by replacing the lens in a conventional camera with an amplitude mask and computational reconstruction algorithms. These lensless cameras, called FlatCams can be less than a millimeter in thickness and enable applications where size, weight, thickness or cost are the driving factors. Second, we discuss high-resolution, long-distance imaging using Fourier Ptychography, where the need for a large aperture aberration corrected lens is replaced by a camera array and associated phase retrieval algorithms resulting again in order of magnitude reductions in size, weight and cost. Finally, I will spend a few minutes discussing how the wholistic computational imaging approach can be used to create ultra-high-resolution wavefront sensors.
- MERL is excited to announce the first keynote speaker for our Virtual Open House 2021:
See All News & Events for Robotics -
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Internships
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CV1721: Deep Learning for Robotic Grasping
MERL is looking for a highly motivated and qualified intern to work on computer vision for robotic grasping. The ideal candidate would be a Ph.D. student with a strong background in deep learning and robotics. There are several available topics for consideration including goal-driven grasping of novel objects, grasping for bin picking, and grasping in clutter. The project requires development of novel algorithms which can be implemented and evaluated on a robotic platform. Experience in working with a physics engine simulator like PyBullet, Mujoco, or Gazebo is preferred. 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.
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MS1776: Control for Precise Robotic Assembly
MERL seeks a highly motivated intern to perform original research on advanced dynamic control for high-precision robotic assembly. The research will involve developing new model-based algorithms for task planning, path planning, and hybrid nonlinear control of contacts and collisions that occur during assembly operations, specifically using a new type of low mass, programmable impedance direct-drive delta robot with integrated touch sensing. Masters or Ph.D. students with knowledge and expertise in control theory and robotics, and strong working knowledge of C programming and high-level languages such as Julia or Matlab, and with experience and interest in conducting experimental research are encouraged to apply. Publication of results is expected in both leading conferences and journals. The internship is expected to be 3-6 months in duration, preferably in the summer 2022. This internship requires work that can only be done at MERL, so a remote working arrangement will not be considered. This internship requires work that can only be done at MERL.
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CA1731: Motion planning and control of multi-agent systems
MERL is looking for a highly motivated individual to develop planning and control algorithms for multi-agent systems. The internship will also include experimental validation of the proposed algorithms in various robotic testbeds (quadrotors and mini-cars) at MERL. The ideal candidate is experienced in multi-agent motion planning and control, and has successfully demonstrated some of their prior work on hardware testbeds. The candidate must be proficient in ROS and C/C++, and at least familiar with Python and MATLAB. Prior experience with crazyflies and/or hamster robots will be considered a plus. The expected duration of the internship is 3-6 months, and the start date is Summer/Fall 2022.
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Openings
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Recent Publications
- , "Model-Based Reinforcement Learning Using Monte Carlo Gradient Estimation", Automatica.it, September 2021.BibTeX TR2021-108 PDF
- @inproceedings{Amadio2021sep,
- author = {Amadio, Fabio and Dalla Libera, Alberto and Carli, Ruggero and Romeres, Diego},
- title = {Model-Based Reinforcement Learning Using Monte Carlo Gradient Estimation},
- booktitle = {Automatica.it},
- year = 2021,
- month = sep,
- url = {https://www.merl.com/publications/TR2021-108}
- }
- , "Visual 3D Perception for Interactive Robotic Tactile Data Acquisition", IEEE International Conference on Automation Science and Engineering (CASE 2021), August 2021.BibTeX TR2021-092 PDF
- @inproceedings{Jain2021aug,
- author = {Jain, Siddarth and Corcodel, Radu and van Baar, Jeroen},
- title = {Visual 3D Perception for Interactive Robotic Tactile Data Acquisition},
- booktitle = {2021 IEEE International Conference on Automation Science and Engineering (CASE)},
- year = 2021,
- month = aug,
- url = {https://www.merl.com/publications/TR2021-092}
- }
- , "Robotic Applications of Mechanical Metamaterials Produced Using SLA 3D Printing: Cthulhu-Morphic Grippers", Solid Freeform Fabrication Symposium, August 2021.BibTeX TR2021-088 PDF
- @inproceedings{Solomon2021aug,
- author = {Solomon, E. and Yerazunis, William S.},
- title = {Robotic Applications of Mechanical Metamaterials Produced Using SLA 3D Printing: Cthulhu-Morphic Grippers},
- booktitle = {Solid Freeform Fabrication Symposium},
- year = 2021,
- month = aug,
- url = {https://www.merl.com/publications/TR2021-088}
- }
- , "Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models", European Control Conference (ECC), June 2021.BibTeX TR2021-068 PDF
- @inproceedings{DallaLibera2021jun,
- author = {Dalla Libera, Alberto and Amadio, Fabio and Nikovski, Daniel N. and Carli, Ruggero and Romeres, Diego},
- title = {Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models},
- booktitle = {European Control Conference (ECC)},
- year = 2021,
- month = jun,
- url = {https://www.merl.com/publications/TR2021-068}
- }
- , "Trajectory Optimization for Manipulation of Deformable Objects: Assembly of Belt Drive Units", IEEE International Conference on Robotics and Automation (ICRA), June 2021.BibTeX TR2021-072 PDF
- @inproceedings{Jin2021jun,
- author = {Jin, Shiyu and Romeres, Diego and Raghunathan, Arvind and Jha, Devesh K. and Tomizuka, Masayoshi},
- title = {Trajectory Optimization for Manipulation of Deformable Objects: Assembly of Belt Drive Units},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
- year = 2021,
- month = jun,
- url = {https://www.merl.com/publications/TR2021-072}
- }
- , "Long-Horizon Motion Planning for Autonomous Vehicle Parking Incorporating Incomplete Map Information", IEEE International Conference on Robotics and Automation (ICRA), DOI: 10.1109/ICRA48506.2021.9562101, May 2021, pp. 8135-8142.BibTeX TR2021-062 PDF
- @inproceedings{Dai2021may,
- author = {Dai, Siyu and Wang, Yebin},
- title = {Long-Horizon Motion Planning for Autonomous Vehicle Parking Incorporating Incomplete Map Information},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
- year = 2021,
- pages = {8135--8142},
- month = may,
- publisher = {IEEE},
- doi = {10.1109/ICRA48506.2021.9562101},
- url = {https://www.merl.com/publications/TR2021-062}
- }
- , "Comparison of Three Feedback Modalities for Haptics Sensation in Remote Machine Manipulation", IEEE International Conference on Robotics and Automation (ICRA), May 2021.BibTeX TR2021-063 PDF Video
- @inproceedings{Haruna2021may,
- author = {Haruna, Masaki and Kawaguchi, Noboru and Ogino, Masaki and Koike-Akino, Toshiaki},
- title = {Comparison of Three Feedback Modalities for Haptics Sensation in Remote Machine Manipulation},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
- year = 2021,
- month = may,
- url = {https://www.merl.com/publications/TR2021-063}
- }
- , "Tactile-RL for Insertion: Generalization to Objects of Unknown Geometry", IEEE International Conference on Robotics and Automation (ICRA), May 2021.BibTeX TR2021-052 PDF
- @inproceedings{Dong2021may,
- author = {Dong, Siyuan and Jha, Devesh K. and Romeres, Diego and Kim, Sangwoon and Nikovski, Daniel N. and Rodriguez, Alberto},
- title = {Tactile-RL for Insertion: Generalization to Objects of Unknown Geometry},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
- year = 2021,
- month = may,
- url = {https://www.merl.com/publications/TR2021-052}
- }
- , "Model-Based Reinforcement Learning Using Monte Carlo Gradient Estimation", Automatica.it, September 2021.
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Videos
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[MERL Seminar Series 2021] Learning to See by Moving: Self-supervising 3D scene representations for perception, control, and visual reasoning
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Robotic Research at MERL
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Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models
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Modelica-Based Modeling and Control of a Delta Robot
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Towards Human-Level Learning of Complex Physical Puzzles
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Assembly of Belt Drive Units
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Examples of Robotic Manipulation
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Tactile-RL for Insertion: Generalization to Objects of Unknown Geometry
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Cooperating Modular Goal Selection and Motion Planning for Autonomous Driving
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Deep Reactive Planning in Dynamic Environments
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Monte Carlo Probabilistic Inference for Learning Control
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Experimental Validation of Reachability-based Decision Making for Autonomous Driving
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Software Downloads
