We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot. This is made possible by two key components: a novel algorithm, which we call automatic domain randomization (ADR) and a robot platform built for machine learning. ADR automatically generates a distribution over randomized environments of ever-increasing difficulty. Control policies and vision state estimators trained with ADR exhibit vastly improved sim2real transfer. For control policies, memory-augmented models trained on an ADR-generated distribution of environments show clear signs of emergent meta-learning at test time. The combination of ADR with our custom robot platform allows us to solve a Rubik’s cube with a humanoid robot hand, which involves both control and state estimation problems. Videos summarizing our results are available: https://openai.com/blog/solving-rubiks-cube/ Read More
Tag Archives: Robotics
How “Cobots” Are Transforming Jobs in Every Industry, from Fast Food to Law
A recent estimation put 40% of the world’s jobs at risk of automation over the next 15 years. That’s a major shift, but it’s nothing new — throughout history, advances in technology have replaced human jobs time and again. Between 1947 and 2014, for example, the number of U.S. workers employed by the railroad industry dropped by 86% as a result of new technology and automation. At the same time, this tech dramatically increased productivity, allowing the amount of freight being moved to increase by 182%.
Today it’s the field of robotics — or rather, “cobotics” — that’s changing the way we work. ….
HBO Vice’s recent Special Report: The Future of Work took a closer look at what all this automation means for employees and companies alike. Read More
Will robots really steal our jobs?
A new PricewaterhouseCoopers report analyzes the long-term impacts of AI and automation, dividing the future of automation into three “waves:” the algorithm wave, extending into the early 2020s; the augmentation wave, into the late 2020s, and the autonomy wave, extending into the mid-2030s as described in Table 1.1 below.
Creating the Intelligent Asset: Fusing IoT, Robotics, and Artificial Intelligence
World Economic Forum – 8 predictions on AI & Robotics
Coming Soon to a Battlefield: Robots That Can Kill
Wallops island—a remote, marshy spit of land along the eastern shore of Virginia, near a famed national refuge for horses—is mostly known as a launch site for government and private rockets. But it also makes for a perfect, quiet spot to test a revolutionary weapons technology.
If a fishing vessel had steamed past the area last October, the crew might have glimpsed half a dozen or so 35-foot-long inflatable boats darting through the shallows, and thought little of it. But if crew members had looked closer, they would have seen that no one was aboard: The engine throttle levers were shifting up and down as if controlled by ghosts. The boats were using high-tech gear to sense their surroundings, communicate with one another, and automatically position themselves so, in theory, .50-caliber machine guns that can be strapped to their bows could fire a steady stream of bullets to protect troops landing on a beach.
The secretive effort—part of a Marine Corps program called Sea Mob—was meant to demonstrate that vessels equipped with cutting-edge technology could soon undertake lethal assaults without a direct human hand at the helm. Read More
Microsoft Icecaps: An open-source toolkit for conversation modeling
Icecaps provides an array of capabilities from recent conversation modeling literature. Several of these tools were driven by recent work done here at Microsoft Research, including personalization embeddings, maximum mutual information–based decoding, knowledge grounding, and an approach for enforcing more structure on shared feature representations to encourage more diverse and relevant responses. Our library leverages TensorFlow in a modular framework designed to make it easy for users to construct sophisticated training configurations using multi-task learning. In the coming months, we’ll equip Icecaps with pre-trained conversational models that researchers and developers can either use directly out of the box or quickly adapt to new scenarios by bootstrapping their own systems. Read More
SIGGRAPH 2018: DeepMimic paper
Researchers use machine learning to teach robots how to trek through unknown terrains
A team of Australian researchers has designed a reliable strategy for testing physical abilities of humanoid robots—robots that resemble the human body shape in their build and design. Using a blend of machine learning methods and algorithms, the research team succeeded in enabling test robots to effectively react to unknown changes in the simulated environment, improving their odds of functioning in the real world.
The findings, which were published in a joint publication of the IEEE and the Chinese Association of Automation Journal of Automatica Sinica in July, have promising implications in the broad use of humanoid robots in fields such as healthcare, education, disaster response and entertainment Read More
Fulltext of the paper is available:
http://www.ieee-jas.org/article/doi/10.1109/JAS.2019.1911567?pageType=en
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