News & Events

12 were found.




  •  NEWS   New robotics benchmark system
    Date: November 16, 2020
    MERL Contacts: Devesh Jha; Daniel Nikovski; Diego Romeres; Alan Sullivan; Jeroen van Baar
    Research Areas: Artificial Intelligence, Machine Learning, Robotics
    Brief
    • MERL researchers, in collaboration with researchers from MELCO and the Department of Brain and Cognitive Science at MIT, have released simulation software Circular Maze Environment (CME). This system could be used as a new benchmark for evaluating different control and robot learning algorithms. The control objective in this system is to tip and the tilt the maze so as to drive one (or multiple) marble(s) to the innermost ring of the circular maze. Although the system is very intuitive for humans to control, it is very challenging for artificial intelligence agents to learn efficiently. It poses several challenges for both model-based as well as model-free methods, due to its non-smooth dynamics, long planning horizon, and non-linear dynamics. The released Python package provides the simulation environment for the circular maze, where movement of multiple marbles could be simulated simultaneously. The package also provides a trajectory optimization algorithm to design a model-based controller in simulation.
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  •  NEWS   MERL's Scene-Aware Interaction Technology Featured in Mitsubishi Electric Corporation Press Release
    Date: July 22, 2020
    Where: Tokyo, Japan
    MERL Contacts: Anoop Cherian; Chiori Hori; Takaaki Hori; Jonathan Le Roux; Tim K. Marks; Alan Sullivan; Anthony Vetro
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
    Brief
    • Mitsubishi Electric Corporation announced that the company has developed what it believes to be the world’s first technology capable of highly natural and intuitive interaction with humans based on a scene-aware capability to translate multimodal sensing information into natural language.

      The novel technology, Scene-Aware Interaction, incorporates Mitsubishi Electric’s proprietary Maisart® compact AI technology to analyze multimodal sensing information for highly natural and intuitive interaction with humans through context-dependent generation of natural language. The technology recognizes contextual objects and events based on multimodal sensing information, such as images and video captured with cameras, audio information recorded with microphones, and localization information measured with LiDAR.

      Scene-Aware Interaction for car navigation, one target application, will provide drivers with intuitive route guidance. The technology is also expected to have applicability to human-machine interfaces for in-vehicle infotainment, interaction with service robots in building and factory automation systems, systems that monitor the health and well-being of people, surveillance systems that interpret complex scenes for humans and encourage social distancing, support for touchless operation of equipment in public areas, and much more. The technology is based on recent research by MERL's Speech & Audio and Computer Vision groups.


      Demonstration Video:



      Link:

      Mitsubishi Electric Corporation Press Release
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  •  AWARD   R&D100 award for Deep Learning-based Water Detector
    Date: November 16, 2018
    Awarded to: Ziming Zhang, Alan Sullivan, Hideaki Maehara, Kenji Taira, Kazuo Sugimoto
    MERL Contact: Alan Sullivan
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    Brief
    • Researchers and developers from MERL, Mitsubishi Electric and Mitsubishi Electric Engineering (MEE) have been recognized with an R&D100 award for the development of a deep learning-based water detector. Automatic detection of water levels in rivers and streams is critical for early warning of flash flooding. Existing systems require a height gauge be placed in the river or stream, something that is costly and sometimes impossible. The new deep learning-based water detector uses only images from a video camera along with 3D measurements of the river valley to determine water levels and warn of potential flooding. The system is robust to lighting and weather conditions working well during the night as well as during fog or rain. Deep learning is a relatively new technique that uses neural networks and AI that are trained from real data to perform human-level recognition tasks. This work is powered by Mitsubishi Electric's Maisart AI technology.
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  •  NEWS   MERL Researchers Demonstrate Robot Learning Technology at CEATEC'18
    Date: October 15, 2018 - October 19, 2018
    Where: CEATEC'18, Makuhari Messe, Tokyo
    MERL Contacts: Devesh Jha; Daniel Nikovski; Diego Romeres; Alan Sullivan; Jeroen van Baar; William Yerazunis
    Research Areas: Artificial Intelligence, Computer Vision, Data Analytics, Robotics
    Brief
    • MERL's work on robot learning algorithms was demonstrated at CEATEC'18, Japan's largest IT and electronics exhibition and conference held annually at Makuhari Messe near Tokyo. A team of researchers from the Data Analytics Group at MERL and the Artificial Intelligence Department of the Information Technology Center (ITC) of MELCO presented an interactive demonstration of a model-based artificial intelligence algorithm that learns how to control equipment autonomously. The algorithm developed at MERL constructs models of mechanical equipment through repeated trial and error, and then learns control policies based on these models. The demonstration used a circular maze, where the objective is to drive a ball to the center of the maze by tipping and tilting the maze, a task that is difficult even for humans; approximately half of the CEATEC'18 visitors who tried to steer the ball by means of a joystick could not bring it to the center of the maze within one minute. In contrast, MERL's algorithm successfully learned how to drive the ball to the goal within ten seconds without the need for human programming. The demo was at the entrance of MELCO's booth at CEATEC'18, inviting visitors to learn more about MELCO's many other AI technologies on display, and was seen by an estimated more than 50,000 visitors over the five days of the expo.
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  •  NEWS   MERL Researchers Demonstrate New Model-Based AI Learning Technology for Equipment Control
    Date: February 14, 2018
    Where: Tokyo, Japan
    MERL Contacts: Devesh Jha; Daniel Nikovski; Diego Romeres; William Yerazunis; Jeroen van Baar; Alan Sullivan
    Research Areas: Optimization, Computer Vision
    Brief
    • New technology for model-based AI learning for equipment control was demonstrated by MERL researchers at a recent press release event in Tokyo. The AI learning method constructs predictive models of the equipment through repeated trial and error, and then learns control rules based on these models. The new technology is expected to significantly reduce the cost and time needed to develop control programs in the future. Please see the link below for the full text of the Mitsubishi Electric press release.
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  •  EVENT   MERL to participate in Xconomy Forum on AI & Robotics
    Date & Time: Tuesday, March 28, 2017; 1:30 - 5:30PM
    MERL Contacts: Daniel Nikovski; Alan Sullivan; Jay Thornton; Anthony Vetro; Richard (Dick) Waters; Jinyun Zhang
    Location: Google (355 Main St., 5th Floor, Cambridge MA)
    Brief
    • How will AI and robotics reshape the economy and create new opportunities (and challenges) across industries? Who are the hottest companies that will compete with the likes of Google, Amazon, and Uber to create the future? And what are New England innovators doing to strengthen the local cluster and help lead the national discussion?

      MERL will be participating in Xconomy's third annual conference on AI and robotics in Boston to address these questions. MERL President & CEO, Dick Waters, will be on a panel discussing the status and future of self-driving vehicles. Lab members will also be on hand demonstrate and discuss recent advances AI and robotics technology.

      The agenda and registration for the event can be found online: https://xconomyforum85.eventbrite.com.
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  •  EVENT   MERL hosts Boston Imaging and Vision Meetup
    Date & Time: Tuesday, January 17, 2017; 6:00 pm
    Speaker: Tim Marks, Esra Cansizoglu and Carl Vondrick, MERL and MIT
    MERL Contact: Alan Sullivan
    Location: 201 Broadway, Cambridge, MA
    Research Area: Computer Vision
    Brief
    • MERL was pleased to host the Boston Imaging and Vision Meetup held on January 17. The meetup is an informal gathering of people interested in the field of computer imaging and vision. According to the group's website "the meetup provides an opportunity for the image processing/computer vision community to network, socialize and learn". The event held at MERL featured three speakers, Tim Marks and Esra Cansizoglu from MERL, as well as Carl Vondrick, an MIT CS graduate student in the group of Prof. Antonio Torralba. Roughly 70 people attended to eat pizza, hear the speakers and network.
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  •  NEWS   CIRP CMMO 2013: publication by Alan Sullivan and others
    Date: June 13, 2013
    Where: CIRP Conference on Modeling of Machining Operations (CIRP CMMO)
    MERL Contact: Alan Sullivan
    Research Area: Computer Vision
    Brief
    • The paper "Cutter Workpiece Engagement Calculations for Five-axis Milling using Composite Adaptively Sampled Distance Fields" by Erdim, H. and Sullivan, A. was presented at the CIRP Conference on Modeling of Machining Operations (CIRP CMMO).
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  •  NEWS   Mitsubishi Electric Develops Ultra-High Resolution 3D Shape Representation Technology
    Date: February 12, 2013
    MERL Contact: Alan Sullivan
    Brief
    • Mitsubishi Electric Corporation (TOKYO: 6503) announced today it has developed ultra-high resolution 3D shape representation technology for numerically controlled (NC) machine tools. The technology displays detailed shapes of machined surfaces down to a resolution of 1 micrometer (um) in a 3D machining simulation, which machine operators can use to evaluate surface textures without trial cutting through a high quality machining process.
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  •  NEWS   PMI 2012: publication by Alan Sullivan and others
    Date: October 29, 2012
    Where: CIRP Conference on Process Machine Interactions (PMI)
    MERL Contact: Alan Sullivan
    Research Area: Computer Vision
    Brief
    • The paper "High Accuracy Computation of Geometric Properties of Cutter Workpiece Intersection using Distance Fields for NC Milling" by Erdim, H. and Sullivan, A. was presented at the CIRP Conference on Process Machine Interactions (PMI).
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  •  NEWS   Computer-Aided Design: publication by Ronald N. Perry, Alan Sullivan and others
    Date: June 1, 2012
    Where: Computer-Aided Design
    MERL Contacts: Alan Sullivan; Ronald Perry
    Research Area: Computer Vision
    Brief
    • The article "High Accuracy NC Milling Simulation Using Composite Adaptively Sampled Distance Fields" by Sullivan, A., Erdim, H., Perry, R.N. and Frisken, S.F. was published in Computer-Aided Design.
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  •  NEWS   SPIE Conference on Applications of Digital Image Processing 2009: publication by Anthony Vetro, Alan Sullivan and others
    Date: August 3, 2009
    Where: SPIE Conference on Applications of Digital Image Processing
    MERL Contacts: Alan Sullivan; Anthony Vetro
    Research Area: Digital Video
    Brief
    • The paper "Intermediate View Generation for Perceived Depth Adjustment of Stereo Video" by Arican, Z., Yea, S., Sullivan, A., Vetro, A. and Tescher, A.G. was presented at the SPIE Conference on Applications of Digital Image Processing.
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