In this blog post, we explore a functional paradigm for implementing reinforcement learning (RL) algorithms. The paradigm will be that developers write the numerics of their algorithm as independent, pure functions, and then use a library to compile them into policies that can be trained at scale. We share how these ideas were implemented in RLlib's policy builder API, eliminating thousands of lines of "glue" code and bringing support for Keras and TensorFlow 2.0. One of the key ideas behind functional programming is that programs can be composed largely of pure functions, i.e., functions whose outputs are entirely determined by their inputs. Here less is more: by imposing restrictions on what functions can do, we gain the ability to more easily reason about and manipulate their execution.
"Within the framework of the European project ROBOTT-NET we are developing software and robotic solutions for the prevention and control of rodents in enclosed spaces", says Marco Lorenzo, Service Supervisor at Irabia Control De Plagas. This type of prevention is designed to help technicians and companies have better efficiency and control and a faster response, when it comes to controlling rodent pests. "The project uses a mobile autonomous robotic platform with a robot arm to introduce a camera into the trap. It captures an image that is uploaded to the cloud". "The project is in collaboration with Robotnik, which is responsible for the assembly of the robot; and Hispavista, which is in charge of the cloud part", Marco Lorenzo adds.
Today's commercial aircraft are typically manufactured in sections, often in different locations -- wings at one factory, fuselage sections at another, tail components somewhere else -- and then flown to a central plant in huge cargo planes for final assembly. But what if the final assembly was the only assembly, with the whole plane built out of a large array of tiny identical pieces, all put together by an army of tiny robots? That's the vision that graduate student Benjamin Jenett, working with Professor Neil Gershenfeld in MIT's Center for Bits and Atoms (CBA), has been pursuing as his doctoral thesis work. It's now reached the point that prototype versions of such robots can assemble small structures and even work together as a team to build up a larger assemblies. The new work appears in the October issue of the IEEE Robotics and Automation Letters, in a paper by Jenett, Gershenfeld, fellow graduate student Amira Abdel-Rahman, and CBA alumnus Kenneth Cheung SM '07, PhD '12, who is now at NASA's Ames Research Center, where he leads the ARMADAS project to design a lunar base that could be built with robotic assembly.
The smart city of Milton Keynes hosted the first edition of the European Robotics League (ERL)- Smart Cities Robotic Challenge (SciRoc Challenge). Ten European teams met in the shopping mall of Centre:mk to compete against each other in five futuristic scenarios in which robots assist humans serving coffee orders, picking products in a grocery shop or bringing medical aid. This robotics competition aims at benchmarking robots using a ranking system that allows teams to assess their performance and compare it with others. The European Robotics League (ERL) was launched in 2016 under the umbrella of SPARC- the Partnership for Robotics in Europe. This pan-European robotics competition builds on the success of the EU-funded projects: RoCKIn, euRathlon, EuRoC and ROCKEU2.
From Mexican immigrant to MIT, from Girl Power in Latin America to robotics entrepreneurs in Africa and India, the 2019 annual "women in robotics you need to know about" list is here! We've featured 150 women so far, from 2013 to 2018, and this time we're not stopping at 25. We're featuring 30 badass #womeninrobotics because robotics is growing and there are many new stories to be told. So, without further ado, here are the 30 Women In Robotics you need to know about – 2019 edition! There are 150 more stories on our 2013 to 2018 lists. Why not nominate someone for inclusion next year!
In this episode, Lauren Klein speaks with Dr. Rand Voorhies, co-founder and CTO of inVia Robotics. In a world where consumers expect fast home delivery of a variety of goods, inVia's mission is to help warehouse workers package diverse sets of products quickly using a system of autonomous mobile robots. Voorhies describes how inVia's robots operate to pick and deliver boxes or totes of products to and from people workers in a warehouse environment eliminating the need for people to walk throughout the warehouse, and how the actions of the robots are optimized.
Our work published recently in Science Robotics describes a new form of computer, ideally suited to controlling soft robots. Our Soft Matter Computer (SMC) is inspired by the way information is encoded and transmitted in the vascular system. Soft robotics has exploded in popularity over the last decade. In part, this is because robots made with soft materials can easily adapt and conform to their environment. This makes soft robots particularly suited to tasks that require a delicate touch, such as handling fragile materials or operating close to the (human) body.
One of the biggest urban legends growing up in New York City were rumors about alligators living in the sewers. This myth even inspired a popular children's book called "The Great Escape: Or, The Sewer Story," with illustrations of reptiles crawling out of apartment toilets. To this day, city dwellers anxiously look at manholes wondering what lurks below. This curiosity was shared last month by the US Defense Department with its appeal for access to commercial underground complexes. The US military's research arm, DARPA, launched the Subterranean (or SubT) Challenge in 2017 with the expressed goal of developing systems that enhance "situational awareness capabilities" for underground missions.
In this post, we share some recent promising results regarding the applications of Deep Learning in analog IC design. While this work targets a specific application, the proposed methods can be used in other black box optimization problems where the environment lacks a cheap/fast evaluation procedure. So let's break down how the analog IC design process is usually done, and then how we incorporated deep learning to ease the flow. The intent of analog IC design is to build a physical manufacturable circuit that processes electrical signals in the analog domain, despite all sorts of noise sources that may affect the fidelity of signals. Usually analog circuit design starts off with topology selection.
What would it take to transform a flat sheet into a human face? How would the sheet need to grow and shrink to form eyes that are concave into the face and a convex nose and chin that protrude? How to encode and release complex curves in shape-shifting structures is at the center of research led by the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and Harvard's Wyss Institute of Biologically Inspired Engineering. Over the past decade, theorists and experimentalists have found inspiration in nature as they have sought to unravel the physics, build mathematical frameworks, and develop materials and 3D and 4D-printing techniques for structures that can change shape in response to external stimuli. However, complex multi-scale curvature has remained out of reach.