The company behind ChatGPT is putting together a team capable of developing algorithms to control robots and appears to be hiring roboticists who work specifically on humanoids. OpenAI appears to be ramping up its efforts in robotics, hiring researchers who work on humanoid systems as it explores new ways to advance artificial intelligence . The company has recently recruited a number of researchers with expertise in developing AI algorithms for controlling humanoid and other types of robots. Job listings show that the company is putting together a team capable of creating systems that can be trained through teleoperation and simulation. Sources with knowledge of the company's efforts also say OpenAI is recruiting people to work specifically on humanoid robots, or robots with a partial or full human form.

Atlas, the humanoid robot famous for its parkour and dance routines, has recently begun demonstrating something altogether more subtle but also a lot more significant: It has learned to both walk and grab things using a single artificial intelligence model. What is more, the robot's single learning model is showing some tantalizingly "emergent" skills, like the ability to instinctively recover when it drops an item without having been trained to do so. Boston Dynamics, the company that makes Atlas, together with the Toyota Research Institute (TRI), developed a generalist model that learns to control both arms and legs from a range of example actions. This is different from the norm: robots equipped with the ability to learn would usually rely on one model to walk and jump and another to grasp items. "The feet are just like additional hands, in some sense, to the model," says Russ Tedrake, a roboticist at the Toyota Research Institute and the Massachusetts Institute of Technology, who led the current work.

The union of paleontology and robots has its roots in the more established field of bio-inspired robotics, in which scientists fashion robots based on modern animals. Paleo-roboticists, however, face the added complication of designing robotic systems for which there is no living reference. They work around this limitation by abstracting from the next best option, such as a modern descendant or an incomplete fossil record. To help make sure they're on the right track, they might try to derive general features from modern fauna that radiated from a common ancestor on the evolutionary tree. Or they might turn to good ol' physics to home in on the most plausible ways an animal moved.

A short ferry ride from the port city of Yantai, on the northeast coast of China, sits Genghai No. 1, a 12,000-metric-ton ring of oil-rig-style steel platforms, advertised as a hotel and entertainment complex. Genghai is in fact an unusual tourist destination, one that breeds 200,000 "high-quality marine fish" each year. The vast majority are released into the ocean as part of a process known as marine ranching. The Chinese government sees this work as an urgent and necessary response to the bleak reality that fisheries are collapsing both in China and worldwide. But just how much of a difference can it make? This story is from the latest print edition of MIT Technology Review--it's all about the exciting breakthroughs happening in the world right now.

A disproportionate amount of the primary motor cortex, a region of the brain that controls movement, is devoted to body parts that move in more complicated ways. An especially large portion controls the face and lips; a similarly large portion controls the hands. A human hand is capable of moving in twenty-seven separate ways, more by far than any other body part: our wrists rotate, our knuckles move independently of one another, our fingers can spread or contract. The sensors in the skin of the hand are among the densest in the body, and are part of a network of nerves that run along the spinal cord. "People think of the spinal column as just wires," Arthur Petron, a roboticist who earned his Ph.D. in biomechatronics at M.I.T., said.

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we take a trip to RoboCup2024, see what the International Conference on Machine Learning had in store, and learn about interactive real-world simulators. RoboCup is an international scientific initiative with the goal of advancing the state of the art of intelligent robots. As part of this initiative, a series of competitions and meetings are held throughout the year. The showcase event is an international affair with teams travelling from far and wide to put their machines through their paces.

Roboticists compare robot motions for tasks such as parameter tuning, troubleshooting, and deciding between possible motions. However, most existing visualization tools are designed for individual motions and lack the features necessary to facilitate robot motion comparison. In this paper, we utilize a rigorous design framework to develop Motion Comparator, a web-based tool that facilitates the comprehension, comparison, and communication of robot motions. Our design process identified roboticists' needs, articulated design challenges, and provided corresponding strategies. Motion Comparator includes several key features such as multi-view coordination, quaternion visualization, time warping, and comparative designs. To demonstrate the applications of Motion Comparator, we discuss four case studies in which our tool is used for motion selection, troubleshooting, parameter tuning, and motion review.

There's a well-known observation among roboticists called the Moravec's paradox: What is hard for humans is easy for machines, and what is easy for humans is hard for machines. Thanks to AI, this is now changing. Robots are starting to become capable of doing tasks such as folding laundry, cooking and unloading shopping baskets, which not too long ago were seen as almost impossible tasks. In our most recent cover story for the MIT Technology Review print magazine, I looked at how robotics as a field is at an inflection point. You can read more here.

Imagine activating new robots meant to aid staff in an elder care facility, only to discover the robots are counterproductive. They undermine the most meaningful moments of the jobs and increase staff workloads, because robots demand care too. Eventually, they're returned. This vignette captures key elements of James Adrian Wright's ethnography, "Robots Won't Save Japan", an essential resource for understanding the state of elder care robotics. Wright's rich ethnographic interviews and observations challenge the prevailing funding, research, and development paradigms for robotics. Elder care residents tend to be Disabled, so this review article augments Wrights' insights with overlooked perspectives from Disability and Robotics research. This article highlights how care recipients' portrayal suggests that Paro, a plush robot seal, might perform better than the care team and author indicated -- leading to insights that support urgent paradigm shifts in elder care, ethnographic studies, and robotics. It presents some of the stronger technical status quo counter-arguments to the book's core narratives, then confronts their own assumptions. Furthermore, it explores exceptional cases where Japanese and international roboticists attend to care workers and recipients, justifying key arguments in Wright's compelling book. Finally, it addresses how "Robots won't save Japan" will save Robotics.

Robots are ubiquitous in small-to-large-scale manufacturers. While collaborative robots (cobots) have significant potential in these settings due to their flexibility and ease of use, proper integration is critical to realize their full potential. Specifically, cobots need to be integrated in ways that utilize their strengths, improve manufacturing performance, and facilitate use in concert with human workers. Effective integration requires careful consideration and the knowledge of roboticists, manufacturing engineers, and business administrators. We propose an approach involving the stages of planning, analysis, development, and presentation, to inform manufacturers about cobot integration within their facilities prior to the integration process. We contextualize our approach in a case study with an SME collaborator and discuss insights learned.