MIT researchers have developed a camera-based touch sensor that is long, curved, and shaped like a human finger. Their device, which provides high-resolution tactile sensing over a large area, could enable a robotic hand to perform multiple types of grasps. Imagine grasping a heavy object, like a pipe wrench, with one hand. You would likely grab the wrench using your entire fingers, not just your fingertips. Sensory receptors in your skin, which run along the entire length of each finger, would send information to your brain about the tool you are grasping.

The ability to learn from human demonstration endows robots with the ability to automate various tasks. However, directly learning from human demonstration is challenging since the structure of the human hand can be very different from the desired robot gripper. In this work, we show that manipulation skills can be transferred from a human to a robot through the use of micro-evolutionary reinforcement learning, where a five-finger human dexterous hand robot gradually evolves into a commercial robot, while repeated interacting in a physics simulator to continuously update the policy that is first learned from human demonstration. To deal with the high dimensions of robot parameters, we propose an algorithm for multi-dimensional evolution path searching that allows joint optimization of both the robot evolution path and the policy. Through experiments on human object manipulation datasets, we show that our framework can efficiently transfer the expert human agent policy trained from human demonstrations in diverse modalities to target commercial robots.

A "mechanized infantry division" in the military may take on an entirely different meaning in the future. The idea of enhanced technology being integrated into body armor to produce super soldiers is not a new concept, but a new project launched by the U.S. Air Force could take things to a whole new level. The military branch has given a contract to a robotics firm specializing in artificial intelligence (AI) solutions and which is designed to lead to the introduction of "dexterous robotic systems." Sarcos Defense, a subsidiary of Sarcos Robotics, was awarded the contract. The parent company is dedicated to the development of advanced robotics and electro-mechanical systems.

Artificial intelligence can now solve a Rubik's cube one-handed. The task requires so much dexterity that even humans find the movements difficult. The system was developed by researchers at OpenAI, a technology firm that has previously created an AI that could outplay humans at the video game Dota 2. The team taught an AI to control a commercially available robotic hand developed by the Shadow Robot Company. The AI learned using a technique called reinforcement learning, which involves trial and error. "It starts from not knowing anything about how to move a hand or how a cube would react if you push on the sides or on the faces," says Peter Welinder, part of the team.

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.