Alan Sullivan

Alan Sullivan
  • Biography

    First at U.C. Berkeley, then at Lawrence Livermore National Laboratory, Alan studied interactions between ultra-high intensity femtosecond lasers and plasmas. Prior to joining MERL in 2007, he worked at a series of start-ups where he developed a novel volumetric 3D display technology. At MERL His research interests include computational geometry and computer graphics.

  • Recent News & Events

    •  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: Siheng Chen; Anoop Cherian; Bret Harsham; Chiori Hori; Takaaki Hori; Jonathan Le Roux; Tim 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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  • Awards

    •  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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  • Research Highlights

  • MERL Publications

    •  Wu, Y., Marks, T., Cherian, A., Chen, S., Feng, C., Wang, G., Sullivan, A., "Unsupervised Joint 3D Object Model Learning and 6D Pose Estimation for Depth-Based Instance Segmentation", IEEE ICCV Workshop on Recovering 6D Object Pose, DOI: 10.1109/ICCVW.2019.00339, October 2019, pp. 2777-2786.
      BibTeX TR2019-118 PDF
      • @inproceedings{Wu2019oct,
      • author = {Wu, Yuanwei and Marks, Tim and Cherian, Anoop and Chen, Siheng and Feng, Chen and Wang, Guanghui and Sullivan, Alan},
      • title = {Unsupervised Joint 3D Object Model Learning and 6D Pose Estimation for Depth-Based Instance Segmentation},
      • booktitle = {IEEE ICCV Workshop on Recovering 6D Object Pose},
      • year = 2019,
      • pages = {2777--2786},
      • month = oct,
      • doi = {10.1109/ICCVW.2019.00339},
      • url = {https://www.merl.com/publications/TR2019-118}
      • }
    •  Xu, W., Wang, G., Sullivan, A., Zhang, Z., "Towards Learning Affine-Invariant Representations via Data-Efficient CNNs", arXiv, August 2019.
      BibTeX arXiv
      • @article{Xu2019aug,
      • author = {Xu, Wenju and Wang, Guanghui and Sullivan, Alan and Zhang, Ziming},
      • title = {Towards Learning Affine-Invariant Representations via Data-Efficient CNNs},
      • journal = {arXiv},
      • year = 2019,
      • month = aug,
      • url = {https://arxiv.org/abs/1909.00114}
      • }
    •  Lee, T.-Y., van Baar, J., Wittenburg, K.B., Sullivan, A., "Analysis of the contribution and temporal dependency of LSTM layers for reinforcement learning tasks", IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Explanable AI Workshop, June 2019, pp. 99-102.
      BibTeX TR2019-049 PDF
      • @inproceedings{Lee2019jun2,
      • author = {Lee, Teng-Yok and van Baar, Jeroen and Wittenburg, Kent B. and Sullivan, Alan},
      • title = {Analysis of the contribution and temporal dependency of LSTM layers for reinforcement learning tasks},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Explanable AI Workshop},
      • year = 2019,
      • pages = {99--102},
      • month = jun,
      • url = {https://www.merl.com/publications/TR2019-049}
      • }
    •  van Baar, J., Sullivan, A., Corcodel, R., Jha, D., Romeres, D., Nikovski, D.N., "Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics", IEEE International Conference on Robotics and Automation (ICRA), DOI: 10.1109/ICRA.2019.8793561, May 2019, pp. 6001-6007.
      BibTeX TR2018-202 PDF Video Software
      • @inproceedings{vanBaar2019may,
      • author = {van Baar, Jeroen and Sullivan, Alan and Corcodel, Radu and Jha, Devesh and Romeres, Diego and Nikovski, Daniel N.},
      • title = {Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics},
      • booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
      • year = 2019,
      • pages = {6001--6007},
      • month = may,
      • doi = {10.1109/ICRA.2019.8793561},
      • url = {https://www.merl.com/publications/TR2018-202}
      • }
    •  Cherian, A., Sullivan, A., "Sem-GAN: Semantically-Consistent Image-to-Image Translation", IEEE Winter Conference on Applications of Computer Vision (WACV), DOI: 10.1109/WACV.2019.00196, January 2019.
      BibTeX TR2018-178 PDF
      • @inproceedings{Cherian2019jan,
      • author = {Cherian, Anoop and Sullivan, Alan},
      • title = {Sem-GAN: Semantically-Consistent Image-to-Image Translation},
      • booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
      • year = 2019,
      • month = jan,
      • doi = {10.1109/WACV.2019.00196},
      • url = {https://www.merl.com/publications/TR2018-178}
      • }
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  • Software Downloads

  • Videos

  • MERL Issued Patents

    • Title: "System and Method for Distributed Machining Simulation"
      Inventors: Sullivan, Alan; Lee, Teng-Yok; Thornton, Jay E.
      Patent No.: 10,353,352
      Issue Date: Jul 16, 2019
    • Title: "System and Method for Determining Feedrates of Machining Tools"
      Inventors: Erdim, Huseyin; Sullivan, Alan
      Patent No.: 9,892,215
      Issue Date: Feb 13, 2018
    • Title: "Method and System for Rendering 3D Distance Fields"
      Inventors: Frisken, Sarah F.; Perry, Ronald N.; Sullivan, Alan
      Patent No.: 9,336,624
      Issue Date: May 10, 2016
    • Title: "System and Method for Performing Undo and Redo Operations during Machining Simulation"
      Inventors: Sullivan, Alan; Konobrytskyi, Dmytro
      Patent No.: 9,304,508
      Issue Date: Apr 5, 2016
    • Title: "Hybrid Adaptively Sampled Distance Fields"
      Inventors: Sullivan, Alan
      Patent No.: 9,122,270
      Issue Date: Sep 1, 2015
    • Title: "Analyzing Volume Removed During Machining Simulation"
      Inventors: Erdim, Huseyin; Sullivan, Alan
      Patent No.: 8,935,138
      Issue Date: Jan 13, 2015
    • Title: "System and Method for Simulating Machining Objects"
      Inventors: Sullivan, Alan; Manukyan, Liana
      Patent No.: 8,838,419
      Issue Date: Sep 16, 2014
    • Title: "System and Method for Identifying Defects of Surfaces Due to Machining Processes"
      Inventors: Sullivan, Alan; Yoganandan, Arun R
      Patent No.: 8,532,812
      Issue Date: Sep 10, 2013
    • Title: "System and Method for Optimizing Machining Simulation"
      Inventors: Sullivan, Alan; Yerazunis, William S.
      Patent No.: 8,483,858
      Issue Date: Jul 9, 2013
    • Title: "Volume-Based Coverage Analysis for Sensor Placement in 3D Environments"
      Inventors: Sullivan, Alan; Garaas, Tyler W
      Patent No.: 8,442,306
      Issue Date: May 14, 2013
    • Title: "A Method for Reconstructing a Distance Field of a Swept Volume at a Sample Point"
      Inventors: Frisken, Sarah F.; Perry, Ronald N.; Sullivan, Alan
      Patent No.: 8,265,909
      Issue Date: Sep 11, 2012
    • Title: "A Method for Simulating Numerically Controlled Milling Using Adaptively Sampled Distance Fields"
      Inventors: Frisken, Sarah F.; Perry, Ronald N.; Sullivan, Alan
      Patent No.: 8,010,328
      Issue Date: Aug 30, 2011
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