Rescuing victims from a burning building, a chemical spill, or any disaster that is inaccessible to human responders could one day be a mission for resilient, adaptable robots. Imagine, for instance, rescue-bots that can bound through rubble on all fours, then rise up on two legs to push aside a heavy obstacle or break through a locked door.
By K.N. McGuire, C. De Wagter, K. Tuyls, H.J. Kappen, G.C.H.E. de Croon
Greenhouses, search-and-rescue teams and warehouses are all looking for new methods to enable surveillance in a manner that is quick and safe for the objects and people surrounding them. Many of them already found their way into robotics, but wheeled ground-bound systems have limited maneuverability. Ideally it would be great if flying robots, a.k.a. micro aerial vehicles (MAV) can take advantage of their 3rd dimension to perform surveillance.
As we continue to develop social robots designed for connectedness, we struggle with paradoxes related to authenticity, transience, and replication. In this talk, I will attempt to link together 15 years of experience designing social robots with 100-year-old texts on transience, replication, and the fear of dying. Can there be meaningful relationships with robots who do not suffer natural decay? What would our families look like if we all choose to buy identical robotic family members? Could hand-crafted robotics offer a relief from the mass-replication of the robot’s physical body and thus also from the mass-customization of social experiences?
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. Find out the winning teams of the SciRoc Challenge 2019…
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.
The Mabu robot, with its small yellow body and friendly expression, serves, literally, as the face of the care management startup Catalia Health. The most innovative part of the company’s solution, however, lies behind Mabu’s large blue eyes.
“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.
By Eric Liang and Richard Liaw and Clement Gehring
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.
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 inspiring #womeninrobotics because robotics is growing and there are many new stories to be told.
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.
Dexterous manipulation with multi-fingered hands is a grand challenge in robotics: the versatility of the human hand is as yet unrivaled by the capabilities of robotic systems, and bridging this gap will enable more general and capable robots. Although some real-world tasks (like picking up a television remote or a screwdriver) can be accomplished with simple parallel jaw grippers, there are countless tasks (like functionally using the remote to change the channel or using the screwdriver to screw in a nail) in which dexterity enabled by redundant degrees of freedom is critical. In fact, dexterous manipulation is defined as being object-centric, with the goal of controlling object movement through precise control of forces and motions — something that is not possible without the ability to simultaneously impact the object from multiple directions. For example, using only two fingers to attempt common tasks such as opening the lid of a jar or hitting a nail with a hammer would quickly encounter the challenges of slippage, complex contact forces, and underactuation. Although dexterous multi-fingered hands can indeed enable flexibility and success of a wide range of manipulation skills, many of these more complex behaviors are also notoriously difficult to control: They require finely balancing contact forces, breaking and reestablishing contacts repeatedly, and maintaining control of unactuated objects. Success in such settings requires a sufficiently dexterous hand, as well as an intelligent policy that can endow such a hand with the appropriate control strategy. We study precisely this in our work on Deep Dynamics Models for Learning Dexterous Manipulation.
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.