Terry Dawes for Cantech Letter: Vancouver-based Chrysalix Venture Capital has announced a €100 million fund aimed at driving the global robotics revolution, in partnership withRoboValley, a centre for robotics commercialization based at the Delft University of Technology in the Netherlands.
The RoboValley Fund is Chrysalix’s first robotics fund, and will concentrate on disbursing seed and Series A rounds of funding to early-stage companies developing component technology, intelligent software, and other breakthrough robotics technologies.
“Robotics is predicted to be the next big step in the digital revolution having an unprecedented impact on the way that we live, and provides an answer to some of the grand challenges of the 21st Century,” said RoboValley managing director Arie van den Ende. “Together with Chrysalix long-standing expertise in commercializing early stage industrial innovations, the RoboValley Fund will bring much needed capital and accelerated paths to market for our most promising next generation robotics technologies.” Cont'd...
From the OpenAI team:
We're releasing the public beta of OpenAI Gym, a toolkit for developing and comparing reinforcement learning(RL) algorithms. It consists of a growing suite ofenvironments (from simulated robots to Atari games), and a site for comparing and reproducing results.
OpenAI Gym is compatible with algorithms written in any framework, such asTensorflow and Theano. The environments are written in Python, but we'll soon make them easy to use from any language. We originally built OpenAI Gym as a tool to accelerate our own RL research. We hope it will be just as useful for the broader community. Getting started: If you'd like to dive in right away, you can work through our tutorial... (full intro post)
Jon Excell for The Engineer: Designed by a team at the Max Planck Institute for Intelligent Systems in Stuttgart, the new device is claimed to have considerable advantages over existing pneumatically-powered soft actuators as it doesn’t require a tether.
The device consists of a dielectric elastomer actuator (DEA): a membrane made of hyperelastic material like a latex balloon, with flexible (or ‘compliant’) electrodes attached to each side.
The stretching of the membrane is regulated by means of an electric field between the electrodes, as the electrodes attract each other and squeeze the membrane when voltage is applied. By attaching multiple such membranes, the place of deformation can be shifted controllably in the system. Air is displaced between two chambers.
The membrane material has two stable states. In other words, it can have two different volume configurations at a given pressure without the need to minimize the larger volume. Thanks to this bi-stable state, the researchers are able to move air between a more highly inflated chamber and a less inflated one. They do this by applying an electric current to the membrane of the smaller chamber which responds by stretching and sucking air out of the other bubble. Cont'd...
SoftBank Robotics America Announces New Developer Portal and Android SDK to Boost Expansive Ecosystem of Support for Pepper - the Interactive, Humanoid Robot
Sony Joins Forces with Cogitai to Conduct Research and Development for the Next Wave of Artificial Intelligence
Makeblock, the Ultimate DIY Robotics Construction Platform for Makers & STEM Learners to Showcase its Portfolio Expansion at Maker Faire Bay Area 2016
From Vikash Kumar at University of Washington:
Dexterous hand manipulation is one of the most complex types of biological movement, and has proven very difficult to replicate in robots. The usual approaches to robotic control - following pre-defined trajectories or planning online with reduced models - are both inapplicable. Dexterous manipulation is so sensitive to small variations in contact force and object location that it seems to require online planning without any simplifications. Here we demonstrate for the first time online planning (or model-predictive control) with a full physics model of a humanoid hand, with 28 degrees of freedom and 48 pneumatic actuators. We augment the actuation space with motor synergies which speed up optimization without removing flexibility. Most of our results are in simulation, showing nonprehensile object manipulation as well as typing. In both cases the input to the system is a high level task description, while all details of the hand movement emerge online from fully automated numerical optimization. We also show preliminary results on a hardware platform we have developed "ADROIT" - a ShadowHand skeleton equipped with faster and more compliant actuation... (website)
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