Boston Dynamics has unveiled a new version of its Atlas humanoid robot, showing its creepy movements that make it look like something out of a sci-fi horror movie. The Massachusetts-based robotics company shared a video of the latest humanoid, showing it pulling its leg behind its heads to stand up - in a way that the public said'looked like something out of The Exorcist.' This new version boasts joints that let the machine bend and move in ways that the human body can't - unlike the original, rigid Atlas that was famous for dancing and doing parkour. The company also plans to sell the latest humanoid robot, but the price has yet to be disclosed, and it is set to begin its first job at Hyundai's factories next year. Boston Dynamics announced the new version of its humanoid robot Atlas, featuring a ring light as its face.

Vast industrial investment along with increased academic research on hydraulic heavy-duty manipulators has unavoidably paved the way for their automatization, necessitating the design of robust and high-precision controllers. In this study, an orchestrated robust controller is designed to address the mentioned issue. To do so, the entire robotic system is decomposed into subsystems, and a robust controller is designed at each local subsystem by considering unknown model uncertainties, unknown disturbances, and compound input constraints, thanks to virtual decomposition control (VDC). As such, radial basic function neural networks (RBFNNs) are incorporated into VDC to tackle unknown disturbances and uncertainties, resulting in novel decentralized RBFNNs. All these robust local controllers designed at each local subsystem are, then, orchestrated to accomplish high-precision control. In the end, for the first time in the context of VDC, a semi-globally uniformly ultimate boundedness is achieved under the designed controller. The validity of the theoretical results is verified by performing extensive simulations and experiments on a 6-degrees-of-freedom industrial manipulator with a nominal lifting capacity of $600\, kg$ at $5$ meters reach. Comparing the simulation result to state-of-the-art controller along with provided experimental results, demonstrates that the proposed method established all the promises and performed excellently.

The robotics company that has a knack for viral technology videos showcasing little things robots can do, parkour, bullying robots, and more. A central tenet of Boston Dynamics is the idea of athletic intelligence -- movement patterns that are robust, flexible, and maybe even human. These videos and technologies have gotten to the point where the most popular technology entertainer got a copy and reviewed it, they are for sale, and accessible. The most recent video was trying to showcase a new human style of movement (below). Their focus on athletic intelligence really helped me understand the company, where it fits in with their videos, and why the owners don't stick around.

Soft robots driven by pressurized fluids could explore new frontiers and interact with delicate objects in ways that traditional rigid robots can't. But building entirely soft robots remains a challenge because many of the components required to power these devices are, themselves, rigid. Now, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed electrically-driven soft valves to control hydraulic soft actuators. These valves could be used in assistive and therapeutic devices, bio-inspired soft robots, soft grippers, surgical robots, and more. The research was published in the Proceedings of the National Academy of Sciences (PNAS).

Classical machine learning approaches are sensitive to non-stationarity. Transfer learning can address non-stationarity by sharing knowledge from one system to another, however, in areas like machine prognostics and defense, data is fundamentally limited. Therefore, transfer learning algorithms have little, if any, examples from which to learn. Herein, we suggest that these constraints on algorithmic learning can be addressed by systems engineering. We formally define transfer distance in general terms and demonstrate its use in empirically quantifying the transferability of models. We consider the use of transfer distance in the design of machine rebuild procedures to allow for transferable prognostic models. We also consider the use of transfer distance in predicting operational performance in computer vision. Practitioners can use the presented methodology to design and operate systems with consideration for the learning theoretic challenges faced by component learning systems.

The robotics group at the Institute for Human & Machine Cognition (IHMC) in Pensacola, Fla., has an enormous amount of experience with walking robots. They came in second at the DARPA Robotics Challenge with their Running Man Atlas, one of just three teams to score a perfect 8 out of 8, and they've continued to advance bipedal locomotion using both Atlas and NASA's Valkyrie. We write about their research all the time--just a few months ago, they taught Atlas to walk with straight legs, much like a human does. Humans set a very high standard for bipedal mobility. We're well designed for it in both hardware and software, and we can do some absolutely amazing things.

Trees and other plants, from towering redwoods to diminutive daisies, are nature's hydraulic pumps. They are constantly pulling water up from their roots to the topmost leaves, and pumping sugars produced by their leaves back down to the roots. This constant stream of nutrients is shuttled through a system of tissues called xylem and phloem, which are packed together in woody, parallel conduits. Now engineers at MIT and their collaborators have designed a microfluidic device they call a "tree-on-a-chip," which mimics the pumping mechanism of trees and plants. Like its natural counterparts, the chip operates passively, requiring no moving parts or external pumps.