Building 32 Map
Technische Universität Berlin
Host: Leslie Kaelbling, Tomas Lozano-Perez
The effectiveness of robot interaction depends on the robot's ability to perform task-relevant actions and on the degree to which it is able to predict the outcomes of these actions. In my talk I will argue that the two learning problems---learning actions and learning forward models---must be tightly coupled for each of them to be successful. The presented approach exploits the fact that for an action primitive to be useful it must fulfil two requirements: first, it must have a known and limited variability in continuous parameter space. Second, it must have accurately predictable effects, given the state of the world. This idea is implemented by jointly learning action primitives and symbolic, relational forward models for these primitives. Experiments in a object manipulation task show that coupled learning is required for the robot to understand and solve the given task.
Sebastian Höfer is a 5th year PhD student at the Robotics & Biology laboratory (RBO) at Technical University Berlin under the guidance of Oliver Brock. His current research focuses on robot learning, particularly at the boundary between the sensorimotor level and high-level symbolic reasoning. He received his master's degree in 2011 from Humboldt University Berlin. In his thesis he studied how to learn compact sensor space representations for a humanoid robot using unsupervised learning. Before joining the RBO lab, he was working as a research assistant under the supervision of Luc Steels at Sony CSL Paris and VUB Brussels on computational and evolutionary linguistics.
Building 32 Map
Dr. Alex Aravind
The group mutual exclusion problem arises in various applications that require data sharing, and its aim is to achieve exclusive access to shared data while facilitating suitable
concurrency. In this talk, a simple algorithm that we have designed recently to solve group mutual exclusion will be discussed. The algorithms has several nice properties.
Cambridge, MA USA
For approximately two centuries, organic synthesis has generally been conducted in a batch mode (flasks, vessels). Currently, in contrast to nearly all other major manufacturing industries, with few exceptions, pharmaceutical companies utilize batch approaches for synthesis. As economic and environmental pressures have increased, so has interest in continuous processes and continuous manufacturing. Chemistry in flow provides exquisite control over reaction conditions, incorporates continuous separations and in-line recycling of reagents, and because reactor volumes are small compared to batch, significantly enhances safety. Scale-up to large production is achieved not with stepwise transitions to larger and larger vessels, but by knowledge based selection of the appropriate size, running multiple systems in parallel, and adjusting the time a system is in operation. Moreover, a much broader range of reaction conditions (temperature, pressure, and reaction time) and many classes of reactions that are impossible, hazardous, low-throughput, or capricious in batch are safely and conveniently achieved in flow.
This course will focus on the fundamental principles and technologies used in the continuous synthesis and purification of small molecules. The advantages and challenges of flow or continuous manufacturing in comparison to batch for the production of small molecules will be discussed extensively. Advanced topics will include automation, scale-up strategies, cutting-edge methods of synthesis, and purification. Those who complete this course will not only possess a thorough knowledge base, but also will be able to make informed, systematic decisions in selecting between continuous or batch methods for a particular situation or project.
Who Should Attend
This course is designed for scientists and engineers in pharmaceutical and fine chemicals research, development, and manufacturing. The course will be of particular benefit to chemists and chemical engineers who are or are considering implementing continuous flow synthesis into their programs. Those who should attend include:
- Chemists (Discovery/Medicinal and Process Development) and Chemical Engineers in pharmaceutical and fine chemicals research and development
- Chemists and Chemical Engineers in pharmaceutical and fine chemicals manufacturing
- Managers responsible for pharmaceutical fine chemicals research, development, and manufacturing
Cambridge, MA USA
Insanely successful companies, like Apple, Virgin, Toyota, and others, innovate continuously because of their culture of design-thinking. When done right, this thinking links inspiration and passion to execution and delivery—positively affecting every facet of the product and service.
For you to be successful at work, you need to know how to think like a designer when approaching an engineering task alone, but you especially need design-thinking skills when working within a team or leading a team. By applying a design-centered approach you’ll be able to conceive of radically innovative solutions, deeply understand who your real stakeholders are and what they care about, create vision that gets buy-in from senior management and colleagues, avoid hazards, and create solutions that people love both emotionally and intellectually.
Using a 10-step design process and a 3-step vision creation and communication process, you’ll experience the design process first hand in this interactive class that will expand your thinking and help you and your teams create more powerful solutions. You’ll learn how to create materials that align technical and non-technical audiences, understand the vital importance of the psychology behind how people interact with technology, how to manage creativity, and how to assess the effectiveness of your solutions.
WHO SHOULD ATTEND
This course is targeted for design engineers, research engineers, project engineers or managers, product engineers, members of the technical staff, applied scientists, and research scientists. The course would also be of interest to those who supervise early career professionals and those in academia (e.g. engineering and science graduate students, and post-docs).
Cambridge, MA USA
This course on technological innovation will be organized around three modules on (1) Data, (2) Theory, and (3) Application. In the first module, we will analyze new, large data sets on technological improvement, many of which were collected by the instructor and are the most expansive of their kind. We will cover statistical analysis methods and decomposition models in order to extract useful insight on the determinants of technological innovation. Examples from energy conversion, transportation, chemicals, metals, information technology, and a range of other industries will be discussed. In the second module, we will cover theories, that have been developed in recent years and stretching back several decades, to explain technological innovation. We will cover the disciplinary origins of these theories, the empirical evidence for or against them, and the usefulness of these theories for practitioners from various fields including engineering, chemicals, private investment, and public policy. Building on this insight, in the third module we will focus on applying the data analysis methods and theories covered to inform decisions about technology investment and design. The third module will address questions of specific interest to the class. This module will demonstrate the utility of the material covered and how it can be extended to answer a wide range of important questions relating to investment, research and development, manufacturing, and public policy.
Cambridge, MA USA
Are you interested in learning about radar by building and testing your own imaging radar system?
MIT Professional Education is offering a course in the design, fabrication, and testing of a laptop-based radar sensor capable of measuring Doppler and range and forming synthetic aperture radar (SAR) imagery. Lectures will be presented on the topics of applied electromagnetics, antennas, RF design, analog circuits, and digital signal processing while simultaneously building your own radar system and performing field experiments. Each student will receive a radar kit designed by MIT Lincoln Laboratory staff and a course pack.
This course will appeal to those who want to learn how to develop radar systems or SAR imaging, use radar technology, or make components or sub-systems.
During the course you will bring your radar kit into the field and perform experiments such as measuring the speed of passing cars or plotting the range of moving targets. A SAR imaging competition will test your ability to form a SAR image of a target scene of your choice from around campus.
Who Should Attend
This course is targeted for engineers and scientists who plan to design radars; use radar systems in a product or as the final product; work on radar systems, components, or subsystems; or are interested in using radar systems for observation of physical phenomena. Students will learn how radar systems work by attending lectures, building their own radar set, and acquiring radar data in the field. Those who should attend include:
- Developers of radar systems or components
- Users of radar technology
- Purchasers of radar technology such as automotive and government organizations
- Commercial enterprises seeking to use or add radar technology to their product, or develop a radar-based product
- Defense industry or government personnel who want to learn how radar and SAR imaging works
- Defense industry or government supervisors seeking to quickly educate employees
- Unmanned vehicle or robot developers seeking to use radar sensor packages
- Scientists who are interested in using radar technology for the observation of nature
You do not have to be a radar engineer but it helps if you have at least a bachelor?s degree in electrical engineering or physics and are interested in any of the following: electronics, electromagnetics, signal processing, physics, or amateur radio. It is recommended that you have some familiarity with MATLAB. Each student is required to bring a laptop (with a stereo-audio input) with MATLAB, because this will be used for data acquisition and signal processing.
Cambridge, MA USA
Systems Engineering, Architecture, and Lifecycle Design: Principles, Models, Tools, and Applications
System and product complexities are increasing with time due to requirements for additional functionality, higher performance, competitive cost, schedule pressures, more flexibility or adaptability, and cognition-based friendlier human interface. Academics and practitioners alike have come to realize that complex engineering systems have a set of common principles, embedded in a theory, that goes beyond and cuts across the traditional fields of engineering. Novel products and systems development require the involvement of and communication between professionals with multiple disciplinary backgrounds and other stakeholders, notably the customer. This collaboration increases the likelihood of detecting product failures early on during its lifecycle, yielding significant cuts in time to market and heavy rework expenses.
The Systems Engineering discipline has been continuously growing in response to the increase in system and product complexity. System architecture is an early critical lifecycle activity that determines the system's concept and model of operation. Nurturing systems thinking and engineering skills, the engineering education this course provides grounds intuition and experience in theory and practice. We start with general SE and Systems Architecture principles. We then introduce SysML – the new SE standard from OMG. The approach underlying the system modeling is Object-Process Methodology (OPM), a comprehensive approach to systems architecting, conceptual modeling, and lifecycle support. An integrated engineering software environment, OPCAT, which combines intuitive graphics with an automatically-generated subset of English, implements OPM and supports the modeling of the system's requirements, top-level architecture, analysis and design models that are amenable to simulation and deployment. The resulting model can be translated to SysML and it constitutes a central underlying artifact of the system, which evolves and serves as a major reference to all the stakeholders throughout the entire lifecycle.
WHO SHOULD ATTEND
This program is intended for system architects, systems engineers, software engineers, system integrators, analysts and designers, executives, product developers, project leaders, project heads, systems biologists, banking and financial engineers and modelers, methods engineers, and database designers and administrators.