The game of Jenga represents an inspiring benchmark for developing innovative manipulation solutions for complex tasks. Indeed, it encouraged the study of novel robotics methods to successfully extract blocks from the tower. A Jenga game round undoubtedly embeds many traits of complex industrial or surgical manipulation tasks, requiring a multi-step strategy, the combination of visual and tactile data, and the highly precise motion of the robotic arm to perform a single block extraction. In this work, we propose a novel, cost-effective architecture for playing Jenga with e.Do, a 6-DOF anthropomorphic manipulator manufactured by Comau, a standard depth camera, and an inexpensive monodirectional force sensor. Our solution focuses on a visual-based control strategy to accurately align the end-effector with the desired block, enabling block extraction by pushing. To this aim, we train an instance segmentation deep learning model on a synthetic custom dataset to segment each piece of the Jenga tower, allowing visual tracking of the desired block's pose during the motion of the manipulator. We integrate the visual-based strategy with a 1D force sensor to detect whether the block can be safely removed by identifying a force threshold value. Our experimentation shows that our low-cost solution allows e.DO to precisely reach removable blocks and perform up to 14 consecutive extractions in a row.

A human being has controlled a robot hand from more than 5,000 miles away in a world first that could revolutionise the world of robotics. The robot successfully transmitted the feeling of touch across the Atlantic, from California to London, allowing for instantaneous and lifelike remote control. A demonstrator - equipped with a special glove - picked up balls, typed words on a keyboard and played chess in real time. Three tech firms joined forces on the project which simulates touch and allows for long-distance control of robots. Future applications, experts say, include bomb disposal, space exploration and breakthroughs in methods of communication.

In this week's edition of the NVIDIA Developer Top 5 video, we revisit the top developer stories of the week. From an AI system that can automatically map vegetation areas in the Arctic to a robot that relies on AI to play Jenga.

Using machine-learning and sensory hardware, Alberto Rodriguez, assistant professor of mechanical engineering, and members of MIT's MCube lab have developed a robot that is learning how to play the game Jenga . The technology could be used in robots for manufacturing assembly lines.

The humble game of Jenga has become the latest human pursuit to fall to machines, scientists have announced. In what marks significant progress for robotic manipulation of real-world objects, a Jenga-playing machine can learn the complex physics involved in withdrawing wooden blocks from a tower through physical trial and error. This differentiates it from robots that have mastered purely cognitive games such as chess and Go through visual cues. "Playing the game of Jenga also requires mastery of physical skills such as probing, pushing, pulling, placing and aligning pieces," said Prof Alberto Rodriguez from the department of mechanical engineering at Massachusetts Institute of Technology. Combining interactive perception and manipulation – whereby the robot would touch the tower to learn how and when to move blocks – is extremely difficult to simulate and therefore the robot has to learn in the real world, he added.

Despite dazzling advances in AI, robots are still horribly ham-fisted. Increasingly, researchers and companies are turning to machine learning to make them more adaptive and dexterous. This typically means feeding the robot a video of what's in front of it and asking it to work out how it should move in order to manipulate that object. For instance, researchers at OpenAI, a nonprofit in San Francisco, taught a robotic hand to manipulate a child's block in this way. By signing up you agree to receive email newsletters and notifications from MIT Technology Review.

A robotic arm capable of playing the popular game Jenga has been built by American engineers. The machine, developed by MIT engineers, is equipped with a soft-pronged gripper, a force-sensing wrist cuff and an external camera. This enables it to see and feel the movement of the tower and adjust for each individual block. It monitors and tracks the feedback from the blocks and the machine makes subtle adjustments to avoid toppling the tower and losing the game. A computer takes in visual and tactile feedback via the cameras and cuff, and compares these measurements to moves that the robot previously made.

These are a few of the things that give humans debilitating anxiety. Robots can't solve any of these problems for us, but one machine can now brave the angst that is the crumbling tower of wooden blocks: Researchers at MIT report today in Science Robotics that they've engineered a robot to teach itself the complex physics of Jenga. This, though, is no game--it's a big step in the daunting quest to get robots to manipulate objects in the real world. The process went like this. The researchers equipped an industrial robot arm with a force sensor in its wrist and a two-pronged manipulator, and sat it down in front of a Jenga tower.