News & Events

96 News items, Awards, Events or Talks found.



Learn about the MERL Seminar Series.



  •  NEWS    Yebin Wang delivered an invited industry talk at the 1st IEEE Industrial Electronics Society Annual On-Line Conference
    Date: December 9, 2022 - December 11, 2022
    MERL Contact: Yebin Wang
    Research Areas: Communications, Control, Optimization
    Brief
    • Future factory, in the era of industry 4.0, is characterized by autonomy, digital twin, and mass customization. This talk, titled "Future factory automation and cyber-physical system: an industrial perspective," focuses on tackling the challenges arising from mass customization, for example reconfigurable machine controller and material flow.
  •  
  •  NEWS    MERL researchers presenting workshop papers at NeurIPS 2022
    Date: December 2, 2022 - December 8, 2022
    MERL Contacts: Matthew E. Brand; Toshiaki Koike-Akino; Jing Liu; Saviz Mowlavi; Kieran Parsons; Ye Wang
    Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Signal Processing
    Brief
    • In addition to 5 papers in recent news (https://www.merl.com/news/news-20221129-1450), MERL researchers presented 2 papers at the NeurIPS Conference Workshop, which was held Dec. 2-8. NeurIPS is one of the most prestigious and competitive international conferences in machine learning.

      - “Optimal control of PDEs using physics-informed neural networks” by Saviz Mowlavi and Saleh Nabi

      Physics-informed neural networks (PINNs) have recently become a popular method for solving forward and inverse problems governed by partial differential equations (PDEs). By incorporating the residual of the PDE into the loss function of a neural network-based surrogate model for the unknown state, PINNs can seamlessly blend measurement data with physical constraints. Here, we extend this framework to PDE-constrained optimal control problems, for which the governing PDE is fully known and the goal is to find a control variable that minimizes a desired cost objective. We validate the performance of the PINN framework by comparing it to state-of-the-art adjoint-based optimization, which performs gradient descent on the discretized control variable while satisfying the discretized PDE.

      - “Learning with noisy labels using low-dimensional model trajectory” by Vasu Singla, Shuchin Aeron, Toshiaki Koike-Akino, Matthew E. Brand, Kieran Parsons, Ye Wang

      Noisy annotations in real-world datasets pose a challenge for training deep neural networks (DNNs), detrimentally impacting generalization performance as incorrect labels may be memorized. In this work, we probe the observations that early stopping and low-dimensional subspace learning can help address this issue. First, we show that a prior method is sensitive to the early stopping hyper-parameter. Second, we investigate the effectiveness of PCA, for approximating the optimization trajectory under noisy label information. We propose to estimate the low-rank subspace through robust and structured variants of PCA, namely Robust PCA, and Sparse PCA. We find that the subspace estimated through these variants can be less sensitive to early stopping, and can outperform PCA to achieve better test error when trained on noisy labels.

      - In addition, new MERL researcher, Jing Liu, also presented a paper entitled “CoPur: Certifiably Robust Collaborative Inference via Feature Purification" based on his previous work before joining MERL. His paper was elected as a spotlight paper to be highlighted in lightening talks and featured paper panel.
  •  
  •  AWARD    Arvind Raghunathan receives Roberto Tempo Best CDC Paper Award at 2022 IEEE Conference on Decision & Control (CDC)
    Date: December 8, 2022
    Awarded to: Arvind Raghunathan
    MERL Contact: Arvind Raghunathan
    Research Areas: Control, Optimization
    Brief
    • Arvind Raghunathan, Senior Principal Research Scientist in the Data Analytics group, received the IEEE Control Systems Society Roberto Tempo Best CDC Paper Award. The award was presented at the 2022 IEEE Conference on Decision & Control (CDC).

      The award is given annually in honor of Roberto Tempo, the 44th President of the IEEE Control Systems Society (CSS). The Tempo Award Committee selects the best paper from the previous year's CDC based on originality, potential impact on any aspect of control theory, technology, or implementation, and for the clarity of writing. This year's award committee was headed by Prof. Patrizio Colaneri, Politecnico di Milano. Arvind's paper was nominated for the award by Prof. Lorenz Biegler, Carnegie Mellon University, with supporting letters from Prof. Andreas Waechter, Northwestern University, and Prof. Victor Zavala, University of Wisconsin-Madison.
  •  
  •  NEWS    MERL Researchers Presented Six Papers at the 2022 IEEE Conference on Decision and Control (CDC’22)
    Date: December 6, 2022 - December 9, 2022
    Where: Cancún, Mexico
    MERL Contacts: Mouhacine Benosman; Karl Berntorp; Ankush Chakrabarty; Marcus Greiff; Devesh K. Jha; Arvind Raghunathan; Diego Romeres; Yebin Wang
    Research Areas: Control, Optimization
    Brief
    • MERL researchers presented six papers at the Conference on Decision and Control that was held in Cancún, Mexico from December 6-9, 2022. The papers covered a broad range of topics in the areas of decision making and control, including Bayesian optimization, quadratic programming, solution of differential equations, distributed Kalman filtering, thermal monitoring of batteries, and closed-loop control optimization.
  •  
  •  NEWS    Karl Berntorp gave Spotlight Talk at CDC Workshop on Gaussian Process Learning-Based Control
    Date: December 5, 2022
    Where: Cancun, Mexico
    MERL Contact: Karl Berntorp
    Research Areas: Control, Machine Learning
    Brief
    • Karl Berntorp was an invited speaker at the workshop on Gaussian Process Learning-Based Control organized at the Conference on Decision and Control (CDC) 2022 in Cancun, Mexico.

      The talk was part of a tutorial-style workshop aimed to provide insight into the fundamentals behind Gaussian processes for modeling and control and sketching some of the open challenges and opportunities using Gaussian processes for modeling and control. The talk titled ``Gaussian Processes for Learning and Control: Opportunities for Real-World Impact" described some of MERL's efforts in using Gaussian processes (GPs) for learning and control, with several application examples and discussing some of the key benefits and limitations with using GPs for learning-based control.
  •  
  •  EVENT    MERL's Virtual Open House 2022
    Date & Time: Monday, December 12, 2022; 1:00pm-5:30pm ET
    Location: Mitsubishi Electric Research Laboratories (MERL)/Virtual
    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
    Brief
    • Join MERL's virtual open house on December 12th, 2022! Featuring a keynote, live sessions, research area booths, and opportunities to interact with our research team. Discover who we are and what we do, and learn about internship and employment opportunities.
  •  
  •  NEWS    Rien Quirynen to give an invited talk at the University of California Santa Cruz
    Date: November 14, 2022
    Where: Zoom
    MERL Contact: Rien Quirynen
    Research Areas: Control, Dynamical Systems, Optimization, Robotics
    Brief
    • Rien Quirynen will give an invited talk at the Electrical and Computer Engineering Department, University of California Santa Cruz on "Real-time Motion Planning and Predictive Control by Mixed-integer Programming for Autonomous Vehicles". The talk will present recent work on a tailored branch-and-bound method for real-time motion planning and decision making on embedded processing units, and recent results for two applications related to automated driving and traffic control.
  •  
  •  NEWS    Avishai Weiss to give an invited talk at the University of Kentucky
    Date: November 11, 2022
    MERL Contact: Avishai Weiss
    Research Areas: Control, Dynamical Systems, Optimization
    Brief
    • Avishai Weiss will give an invited talk at the William Maxwell Reed Seminar Series, Mechanical and Aerospace Engineering Department, University of Kentucky on "Fail-Safe Spacecraft Rendezvous." The talk will present some recent developments at MERL on guaranteeing safe rendezvous trajectories that avoid colliding with the target in the event of thruster anomalies.
  •  
  •  TALK    [MERL Seminar Series 2022] Prof. Ufuk Topcu presents talk titled Autonomous systems in the intersection of formal methods, learning, and control
    Date & Time: Wednesday, October 26, 2022; 1:00 PM
    Speaker: Ufuk Topcu, The University of Texas at Austin
    MERL Host: Abraham P. Vinod
    Research Areas: Control, Dynamical Systems, Optimization
    Abstract
    • Autonomous systems are emerging as a driving technology for countlessly many applications. Numerous disciplines tackle the challenges toward making these systems trustworthy, adaptable, user-friendly, and economical. On the other hand, the existing disciplinary boundaries delay and possibly even obstruct progress. I argue that the nonconventional problems that arise in designing and verifying autonomous systems require hybrid solutions in the intersection of learning, formal methods, and controls. I will present examples of such hybrid solutions in the context of learning in sequential decision-making processes. These results offer novel means for effectively integrating physics-based, contextual, or structural prior knowledge into data-driven learning algorithms. They improve data efficiency by several orders of magnitude and generalizability to environments and tasks that the system had not experienced previously.
  •  
  •  NEWS    Invited talk at The Penn State Seminar Series on Systems, Control, and Robotics.
    Date: October 20, 2022
    Where: University Park, PA
    MERL Contact: Devesh K. Jha
    Research Areas: Artificial Intelligence, Control, Robotics
    Brief
    • Devesh Jha, a Principal Research Scientist in the Data Analytics Group at MERL, delivered an invited talk at The Penn State Seminar Series on Systems, Control and Robotics. This talk presented some of the recent work done at MERL in the areas of optimization and control for robotic manipulation in unstructured environment.
  •  
  •  NEWS    Stefano Di Cairano to give a public lecture on status and challenges of automotive driving at IEEE CSS Day
    Date: October 24, 2022
    Where: Online, 10/24/2022 9:00am (Eastern time)
    MERL Contact: Stefano Di Cairano
    Research Areas: Control, Dynamical Systems, Optimization, Robotics
    Brief
    • Dr. Stefano Di Cairano (Senior Team Leader at MERL) has been invited to give a public talk at the first IEEE CSS Day event on the status, challenges, and role of control in autonomous driving.

      The talk, titled "The Long Voyage Towards Autonomous Driving, with Control Systems as the Co-Pilot", will review some history of autonomous driving, some of the open challenges that control technology may help address, and the next steps towards full-autonomy. The talk is designed for a non-technical audience, to explain the role and impact of control in automated driving technology.
  •  
  •  TALK    A Tunable Control/Learning Framework for Autonomous Systems
    Date & Time: Thursday, October 13, 2022; 1:30pm-2:30pm
    Speaker: Prof. Shaoshuai Mou, Purdue University
    MERL Host: Yebin Wang
    Research Areas: Control, Machine Learning, Optimization
    Abstract
    • Modern society has been relying more and more on engineering advance of autonomous systems, ranging from individual systems (such as a robotic arm for manufacturing, a self-driving car, or an autonomous vehicle for planetary exploration) to cooperative systems (such as a human-robot team, swarms of drones, etc). In this talk we will present our most recent progress in developing a fundamental framework for learning and control in autonomous systems. The framework comes from a differentiation of Pontryagin’s Maximum Principle and is able to provide a unified solution to three classes of learning/control tasks, i.e. adaptive autonomy, inverse optimization, and system identification. We will also present applications of this framework into human-autonomy teaming, especially in enabling an autonomous system to take guidance from human operators, which is usually sparse and vague.
  •  
  •  NEWS    Rien Quirynen gives invited talk at ELO-X Workshop on Embedded Optimization and Learning for Robotics and Mechatronics
    Date: October 10, 2022 - October 11, 2022
    Where: University of Freiburg, Germany
    MERL Contact: Rien Quirynen
    Research Areas: Control, Machine Learning, Optimization
    Brief
    • Rien Quirynen is an invited speaker at an international workshop on Embedded Optimization and Learning for Robotics and Mechatronics, which is organized by the ELO-X project at the University of Freiburg in Germany. This talk, entitled "Embedded learning, optimization and predictive control for autonomous vehicles", presents recent results from multiple projects at MERL that leverage embedded optimization, machine learning and optimal control for autonomous vehicles.

      This workshop is part of the ELO-X Fall School and Workshop. Invited external lecturers will present state-of-the-art techniques and applications in the field of Embedded Optimization and Learning. ELO-X is a Marie Curie Innovative Training Network (ITN) funded by the European Commission Horizon 2020 program.
  •  
  •  NEWS    MERL launches Postdoctoral Research Fellow program
    Date: September 21, 2022
    MERL Contacts: Philip V. Orlik; Anthony Vetro
    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
    Brief
    • Mitsubishi Electric Research Laboratories (MERL) invites qualified postdoctoral candidates to apply for the position of Postdoctoral Research Fellow. This position provides early career scientists the opportunity to work at a unique, academically-oriented industrial research laboratory. Successful candidates will be expected to define and pursue their own original research agenda, explore connections to established laboratory initiatives, and publish high impact articles in leading venues. Please refer to our web page for further details.
  •  
  •  NEWS    Yebin Wang appointed as an Associate Editor for ICRA 2023.
    Date: September 15, 2022
    MERL Contact: Yebin Wang
    Research Areas: Control, Dynamical Systems, Robotics
    Brief
    • Yebin Wang, a Senior Principal Research Scientist in MERL's Electric Machines and Devices, is serving as an Associate Editor for the IEEE International Conference on Robotics and Automation (ICRA) 2023.


      As the flagship conference of the IEEE Robotics and Automation Society, ICRA will bring together the world's top researchers and most important companies to share ideas and advances in our field.
  •  
  •  AWARD    Marcus Greiff receives Outstanding Student Paper Award at CCTA 2022
    Date: August 25, 2022
    Awarded to: Marcus Greiff
    MERL Contact: Marcus Greiff
    Research Areas: Control, Dynamical Systems, Robotics
    Brief
    • Marcus Greiff, a Visiting Research Scientist at MERL, was awarded one of three outstanding student paper awards at the IEEE CCTA 2022 conference for his paper titled "Quadrotor Control on SU(2)xR3 with SLAM Integration". The award was given for originality, clarity, and potential impact on practical applications of control. The work presents a complete UAV control system design, facilitating autonomous supermarket inventorying without the need for external motion capture systems. A video of the experiments is on YouTube, including both simulations and real-time examples.
  •  
  •  NEWS    MERL researchers win ASME Energy Systems Technical Committee Best Paper Award at 2022 American Control Conference
    Date: June 8, 2022
    Where: 2022 American Control Conference
    MERL Contacts: Ankush Chakrabarty; Christopher R. Laughman
    Research Areas: Control, Machine Learning, Multi-Physical Modeling, Optimization
    Brief
    • Researchers from EPFL (Wenjie Xu, Colin Jones) and EMPA (Bratislav Svetozarevic), in collaboration with MERL researchers Ankush Chakrabarty and Chris Laughman, recently won the ASME Energy Systems Technical Committee Best Paper Award at the 2022 American Control Conference for their work on "VABO: Violation-Aware Bayesian Optimization for Closed-Loop Performance Optimization with Unmodeled Constraints" out of 19 nominations and 3 finalists. The paper describes a data-driven framework for optimizing the performance of constrained control systems by systematically re-evaluating how cautiously/aggressively one should explore the search space to avoid sustained, large-magnitude constraint violations while tolerating small violations, and demonstrates these methods on a physics-based model of a vapor compression cycle.
  •  
  •  NEWS    MERL researchers presented 9 papers at the American Control Conference (ACC)
    Date: June 8, 2022 - June 10, 2022
    Where: Atlanta, GA
    MERL Contacts: Karl Berntorp; Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Christopher R. Laughman; Marcel Menner; Rien Quirynen; Abraham P. Vinod; Avishai Weiss
    Research Areas: Control, Machine Learning, Optimization
    Brief
    • At the American Control Conference in Atlanta, GA, MERL presented 9 papers on subjects including autonomous-vehicle decision making and motion planning, realtime Bayesian inference and learning, reference governors for hybrid systems, Bayesian optimization, and nonlinear control.
  •  
  •  TALK    [MERL Seminar Series 2022] Prof. Michael Posa presents talk titled Hybrid robotics and implicit learning
    Date & Time: Tuesday, May 3, 2022; 1:00 PM
    Speaker: Michael Posa, University of Pennsylvania
    MERL Host: Devesh K. Jha
    Research Areas: Control, Optimization, Robotics
    Abstract
    • Machine learning has shown incredible promise in robotics, with some notable recent demonstrations in manipulation and sim2real transfer. These results, however, require either an accurate a priori model (for simulation) or a large amount of data. In contrast, my lab is focused on enabling robots to enter novel environments and then, with minimal time to gather information, accomplish complex tasks. In this talk, I will argue that the hybrid or contact-driven nature of real-world robotics, where a robot must safely and quickly interact with objects, drives this high data requirement. In particular, the inductive biases inherent in standard learning methods fundamentally clash with the non-differentiable physics of contact-rich robotics. Focusing on model learning, or system identification, I will show both empirical and theoretical results which demonstrate that contact stiffness leads to poor training and generalization, leading to some healthy skepticism of simulation experiments trained on artificially soft environments. Fortunately, implicit learning formulations, which embed convex optimization problems, can dramatically reshape the optimization landscape for these stiff problems. By carefully reasoning about the roles of stiffness and discontinuity, and integrating non-smooth structures, we demonstrate dramatically improved learning performance. Within this family of approaches, ContactNets accurately identifies the geometry and dynamics of a six-sided cube bouncing, sliding, and rolling across a surface from only a handful of sample trajectories. Similarly, a piecewise-affine hybrid system with thousands of modes can be identified purely from state transitions. Time permitting, I'll discuss how these learned models can be deployed for control via recent results in real-time, multi-contact MPC.
  •  
  •  TALK    [MERL Seminar Series 2022] Prof. Sebastien Gros presents talk titled RLMPC: An Ideal Combination of Formal Optimal Control and Reinforcement Learning?
    Date & Time: Tuesday, April 12, 2022; 11:00 AM EDT
    Speaker: Sebastien Gros, NTNU
    MERL Host: Rien Quirynen
    Research Areas: Control, Dynamical Systems, Optimization
    Abstract
    • Reinforcement Learning (RL), similarly to many AI-based techniques, is currently receiving a very high attention. RL is most commonly supported by classic Machine Learning techniques, i.e. typically Deep Neural Networks (DNNs). While there are good motivations for using DNNs in RL, there are also significant drawbacks. The lack of “explainability” of the resulting control policies, and the difficulty to provide guarantees on their closed-loop behavior (safety, stability) makes DNN-based policies problematic in many applications. In this talk, we will discuss an alternative approach to support RL, via formal optimal control tools based on Model Predictive Control (MPC). This approach alleviates the issues detailed above, but also presents some challenges. In this talk, we will discuss why MPC is a valid tool to support RL, and how MPC can be combined with RL (RLMPC). We will then discuss some recent results regarding this combination, the known challenges, and the kind of control applications where we believe that RLMPC will be a valuable approach.
  •  
  •  NEWS    Rien Quirynen appointed as an Associate Editor for Journal of Optimal Control Applications and Methods (OCAM)
    Date: February 24, 2022
    MERL Contact: Rien Quirynen
    Research Areas: Control, Optimization
    Brief
    • Rien Quirynen has accepted an invitation to serve on the editorial board of Journal of Optimal Control Applications and Methods (OCAM) as an Associate Editor.

      OCAM provides a forum for papers on the full range of optimal control and related control design methods. The aim is to encourage new developments in optimal control theory and design methodologies that may lead to advances in real control applications.
  •  
  •  NEWS    Dr. Benosman is invited to give the mini-course in control theory at the 2022 edition of the Benelux Meeting on Systems and Control
    Date: July 5, 2022 - July 7, 2022
    MERL Contact: Mouhacine Benosman
    Research Areas: Control, Data Analytics, Dynamical Systems
    Brief
    • The Benelux meeting is an annual conference gathering of the scientific community of Belgium, the Netherlands, and Luxemburg around systems and control. It is especially intended for PhD researchers and a number of activities are dedicated to them, including plenary talks and a mini-course.

      Dr. Benosman has been invited to give the mini-course of the 2022 edition of the conference. This course, entitled 'A hybrid approach to control: classical control theory meets machine learning theory', will be centered around the topic of safe and robust machine learning-based control.
  •  
  •  NEWS    Uroš Kalabić joins editorial board of IEEE Transactions on Control Systems Technology
    Date: December 14, 2021
    Research Area: Control
    Brief
    • MERL researcher Uroš Kalabić has been appointed to serve as an associate editor of the IEEE Transactions on Control Systems Technology.

      The Transactions on Control Systems Technology bridge the gap between the theory and practice of control engineering. They feature publications on engineering needed to implement practical control systems.
  •  
  •  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
    Location: Virtual Event
    Speaker: Prof. Melanie Zeilinger, ETH
    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 Security
    Brief
    • 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.
  •  
  •  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
    Location: Virtual Event
    Speaker: Prof. Ashok Veeraraghavan, Rice University
    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 Security
    Brief
    • 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.
  •