Ourapproach, Exploration through Learned Language Abstraction (ELLA) providesintermediate rewards to an agent for completing relevant low-level behaviors as it tries to solve a complex, sparse rewardtask.

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.

Among the many things we humans like to lord over the rest of the animal kingdom is our complex language. Sure, other creatures talk to one another, but we've got all these wildly complicated written languages with syntax and fun words like defenestrate. This we can also lord over robots, who, in addition to lacking emotion and the ability to not fall on their faces, can't write novels. Researchers at Brown University just got a robot to do something as linguistically improbable as it is beautiful: After training to hand-write Japanese characters, the robot then turned around and started to copy words in a slew of other languages it'd never written before, including Hindi, Greek, and English, just by looking at examples of that handwriting. Not only that, it could do English in print and cursive.

You are free to share this article under the Attribution 4.0 International license. A new algorithm enables robots to put pen to paper, writing words using stroke patterns similar to human handwriting. It's a step, the researchers say, toward robots that are able to communicate more fluently with human coworkers and collaborators. "Just by looking at a target image of a word or sketch, the robot can reproduce each stroke as one continuous action," says Atsunobu Kotani, an undergraduate student at Brown University who led the algorithm's development. "That makes it hard for people to distinguish if it was written by the robot or actually written by a human."

For a serious research robot, Baxter is a charmer. Its face is a flat screen that telegraphs "feelings" like embarrassment (rosy cheeks, upturned eyebrows). If you're so inclined, you can sit in front of it and make it read your mind to fix its mistakes. Or you can point to objects for it to pick up. If it gets confused, it can actually ask you for clarification, a seemingly simple interaction that's in fact a big deal for the budding field of human-robot communication.

It's a dapper robot, wearing a bowtie even while it sits at home in its lab at the University of Washington. Its head is a camera, which it cranes up and down, taking in the view of a dimly lit corner where two computer monitors sit. All perfectly normal stuff for a robot--until the machine speaks: "Hello from the hackers." Clear across the country at Brown University, researchers have compromised Herb2. They've showed how they can scan for internet-connected research robots in labs and take command--with the blessing of the robot's owners at the University of Washington, of course.

The software connects a robot's arms and grippers as well as its onboard cameras and sensors to off-the-shelf virtual reality hardware via the internet. Using handheld controllers, users can control the position of the robot's arms to perform intricate manipulation tasks just by moving their own arms. Users can step into the robot's metal skin and get a first-person view of the environment, or can walk around the robot to survey the scene in the third person -- whichever is easier for accomplishing the task at hand. The data transferred between the robot and the virtual reality unit is compact enough to be sent over the internet with minimal lag, making it possible for users to guide robots from great distances. "We think this could be useful in any situation where we need some deft manipulation to be done, but where people shouldn't be," said David Whitney, a graduate student at Brown who co-led the development of the system.

Inside a brightly decorated lab at the University of California, Berkeley, an ordinary-looking robot has developed an exceptional knack for picking up awkward and unusual objects. What's stunning, though, is that the robot got so good at grasping by working with virtual objects. The robot learned what kind of grip should work for different items by studying a vast data set of 3-D shapes and suitable grasps. The UC Berkeley researchers fed images to a large deep-learning neural network connected to an off-the-shelf 3-D sensor and a standard robot arm. When a new object is placed in front of it, the robot's deep-learning system quickly figures out what grasp the arm should use.

In the Human to Robots Lab, led by Stephanie Tellex, the researchers created an algorithm that let Iorek receive speech commands and process information from human gestures. This is how humans communicate, if we want something we point to it. As with most learning situations, Iorek experienced some problems. If there were similiar items next to each other, it was hard for Iorek to tell which exact object the human was asking for. According to Tellex, in ambiguous situations like these, they want Iorek to ask a question which shows it's confused versus just picking up the wrong object.

Stefanie Tellex, assistant professor of computer science at Brown University, is solving a thorny robotics problem: robotic grasp. She has built a machine learning model so that robots can automatically learn to manipulate objects and can produce much-needed sample data with which other researchers can use to train robots to pick up objects, she explained at the MIT Technology Review's EmTech conference. If you go to a robotics lab and put an object in front of a robot that it has not seen before, that robot will almost always not be able to pick up that object." It's a problem because a robot has to understand the task and the object from sensor information. The robot arm's controls need answers to important questions: what is the object shape, where is it, how should the robotic arm and gripper move into position and where is the right place to grip the object to pick it up?