Reliable contact simulation plays a key role in the development of (semi-)autonomous robots, especially when dealing with contact-rich manipulation scenarios, an active robotics research topic. Besides simulation, components such as sensing, perception, data collection, robot hardware control, human interfaces, etc. are all key enablers towards applying machine learning algorithms or model-based approaches in real world systems. However, there is a lack of software connecting reliable contact simulation with the larger robotics ecosystem (i.e. ROS, Orocos), for a more seamless application of novel approaches, found in the literature, to existing robotic hardware. In this paper, we present the ROS-PyBullet Interface, a framework that provides a bridge between the reliable contact/impact simulator PyBullet and the Robot Operating System (ROS). Furthermore, we provide additional utilities for facilitating Human-Robot Interaction (HRI) in the simulated environment. We also present several use-cases that highlight the capabilities and usefulness of our framework. Please check our video, source code, and examples included in the supplementary material. Our full code base is open source and can be found at https://github.com/cmower/ros_pybullet_interface.

The robot seen here can't see the human arm during the entire dressing process, yet it manages to successfully get a jacket sleeve pulled onto the arm. Robots are already adept at certain things, such as lifting objects that are too heavy or cumbersome for people to manage. Another application they're well suited for is the precision assembly of items like watches that have large numbers of tiny parts -- some so small they can barely be seen with the naked eye. "Much harder are tasks that require situational awareness, involving almost instantaneous adaptations to changing circumstances in the environment," explains Theodoros Stouraitis, a visiting scientist in the Interactive Robotics Group at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). "Things become even more complicated when a robot has to interact with a human and work together to safely and successfully complete a task," adds Shen Li, a PhD candidate in the MIT Department of Aeronautics and Astronautics. Li and Stouraitis -- along with Michael Gienger of the Honda Research Institute Europe, Professor Sethu Vijayakumar of the University of Edinburgh, and Professor Julie A. Shah of MIT, who directs the Interactive Robotics Group -- have selected a problem that offers, quite literally, an armful of challenges: designing a robot that can help people get dressed.

Robots are already adept at certain things, such as lifting objects that are too heavy or cumbersome for people to manage. Another application they're well suited for is the precision assembly of items like watches that have large numbers of tiny parts -- some so small they can barely be seen with the naked eye. "Much harder are tasks that require situational awareness, involving almost instantaneous adaptations to changing circumstances in the environment," explains Theodoros Stouraitis, a visiting scientist in the Interactive Robotics Group at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). "Things become even more complicated when a robot has to interact with a human and work together to safely and successfully complete a task," adds Shen Li, a PhD candidate in the MIT Department of Aeronautics and Astronautics. Li and Stouraitis -- along with Michael Gienger of the Honda Research Institute Europe, Professor Sethu Vijayakumar of the University of Edinburgh, and Professor Julie A. Shah of MIT, who directs the Interactive Robotics Group -- have selected a problem that offers, quite literally, an armful of challenges: designing a robot that can help people get dressed.

Robots are already adept at certain things, such as lifting objects that are too heavy or cumbersome for people to manage. Another application they're well suited for is the precision assembly of items like watches that have large numbers of tiny parts--some so small they can barely be seen with the naked eye. "Much harder are tasks that require situational awareness, involving almost instantaneous adaptations to changing circumstances in the environment," explains Theodoros Stouraitis, a visiting scientist in the Interactive Robotics Group at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). "Things become even more complicated when a robot has to interact with a human and work together to safely and successfully complete a task," adds Shen Li, a Ph.D. candidate in the MIT Department of Aeronautics and Astronautics. Li and Stouraitis--along with Michael Gienger of the Honda Research Institute Europe, Professor Sethu Vijayakumar of the University of Edinburgh, and Professor Julie A. Shah of MIT, who directs the Interactive Robotics Group--have selected a problem that offers, quite literally, an armful of challenges: designing a robot that can help people get dressed. Last year, Li and Shah and two other MIT researchers completed a project involving robot-assisted dressing without sleeves.

When EY Global Chairman and CEO Carmine Di Sibio broke bread with IBM Chairman and CEO Arvind Krishna last July at a luncheon meeting, the two companies were more "frenemies" than partners. It was not a great relationship," said Di Sibio. "We were more competing than we were friends." That all changed when Krishna laid out IBM's new partner ecosystem charge that was taking hold in the wake of IBM's blockbuster acquisition of Red Hat. "The message I got at lunch was IBM was changing, [going through a] transformation, and the Red Hat acquisition was a big piece of this," he said. Di Sibio was impressed and as a result has made a big bet on Krishna and IBM. Now, the New York-based $37.2 billion global consulting powerhouse is aiming to drive $1 billion in revenue from the IBM partnership over the next few years. Di Sibio said he has been heartened by the speed at which Krishna is driving the transformation at IBM. "IBM notoriously has been, I'll say, moving slower," he said. "I do think they have changed, and they are changing. I have confidence they are going to move fast." The Red Hat deal, in fact, has changed the "culture" at IBM and the ecosystem strategy for the better, said Di Sibio. "Their change in strategy really enabled us to have a different type of relationship," he said. Key to building a strong partnership has been Krishna's technology savvy as a leader, his partnership commitment and the trust that has developed between the two executives, said Di Sibio. "Arvind is pretty technical," he said. "I think he is the right choice for where their strategy is going as they move forward." Since that lunch meeting, EY and IBM have combined on a joint go-to-market plan centered on the IBM Financial Services Cloud, combining EY's financial consulting muscle with IBM's cloud prowess. The two companies also launched just two months ago EY Diligence Edge, an AI-enabled M&A due diligence platform hosted on IBM Cloud and supported by IBM Watson Discovery. EY had opportunities to use different cloud providers for EY Diligence Edge but chose IBM because of its hybrid cloud strategy and Watson AI technology as a "differentiator," said Di Sibio. He said the IBM technology is helping win M&A customers for EY. "I think Arvind is bringing IBM back to being an innovative technology company based on hybrid cloud," he said. The EY partnership is just one piece of Krishna's bold bet on partners with the company's biggest go-to-market change in three decades as part of his "maniacal focus" to make IBM the No. 1 provider of hybrid cloud and AI. "I think it's the biggest change we have made in our go-to-market [model] in my living memory," said Krishna, who started his career at IBM in 1990 as a researcher at its Thomas J. Watson Research Center. "If you think about how we pay our people and how we have got clarity on the partners, it is the single biggest change in 30 years on the go-to-market.

From beating us at the game of GO, to recreating paintings by Van Gogh, algorithms are all around us. Artificial Intelligence is no longer science fiction as we are witnessing breakthrough after breakthrough. What most of these innovations have in common is that they make use of a technique called "deep learning." Earlier I had the chance to ask world renowned AI experts about their favorite algorithms in general. This time we will focus on deep learning!

Tech giant Google said it is working with multiple partners to use new-age technologies like artificial intelligence (AI) and machine learning (ML) to improve response time to natural disasters like floods as well as addressing healthcare challenges. Google Technical Project Manager (TensorFlow), Anitha Vijayakumar said the company has developed a system that uses AI for early and accurate flood warnings, including guiding over areas that are dangerous and prone to flooding. Also, Google.org and Googlers are contributing over USD 1 million to support the relief and recovery efforts. Earlier this year, Google had partnered with India's Ministry of Water Resources on a pilot for flood warning based on AI and ML. She said the pilot was undertaken in areas prone to flooding and the initial results are promising.

Tech giant Google today said it is working with multiple partners to use new-age technologies like artificial intelligence (AI) and machine learning (ML) to improve response time to natural disasters like floods as well as addressing healthcare challenges. Google Technical Project Manager (TensorFlow) Anitha Vijayakumar said the company has developed a system that uses AI for early and accurate flood warnings, including guiding over areas that are dangerous and prone to flooding. Earlier this year, Google had partnered with India's Ministry of Water Resources on a pilot for flood warning based on AI and ML. She said the pilot was undertaken in areas prone to flooding and the "initial results are promising". Vijayakumar said the pilot can be extended to other parts of the country and expressed hope that the system will eventually be "able to give longer lead times to people, helping them seek safety".

One of the problems with working out how close we are to creating a replicant, is that it's not clear what these human-like beings are. Director of the new Blade Runner 2049, Denis Villeneuve, has described them as "synthetic humans", which are "not very far from humans". What's clear from the original film is that they are some kind of biorobotic form. Let's for a moment assume they are more like robots than clones and, indeed, in the original Philip K Dick account they are explicitly rogue androids. How close are we to creating an android? Sethu Vijayakumar, Professor of Robotics at the University of Edinburgh, observes that there are two elements to this.

The receptionist at the Institute of Media Innovation, at Singapore's Nanyang Technological University, is a smiling brunette called Nadine. From a distance, nothing about her appearance seems unusual. It's only on closer inspection that doubts set in. Nadine is an "intelligent" robot capable of autonomous behaviour. For a machine, her looks and behaviour are remarkably natural.