You've likely heard that "experience is the best teacher"—but what if learning in the real world is prohibitively expensive? This is the plight of roboticists training their machines on manipulation tasks. Real-world interaction data is costly, so their robots often learn from simulated versions of different activities.
To best assist humans in real-world settings, robots should be able to continuously acquire useful new skills in dynamic and rapidly changing environments. Currently, however, most robots can only tackle tasks that they have been previously trained on and can only acquire new capabilities after further training.
A new robotic tool developed by a team of experts in computer science and biokinesiology could help stroke survivors more accurately track their recovery progress.
Researchers have developed a self-healing robotic gripper for use in soft robotics that is adaptable, recyclable and resilient to damage, thanks to heat-assisted autonomous healing.
Legged robots that artificially replicate the body structure and movements of animals could efficiently complete missions in a wide range of environments, including various outdoor natural settings. To do so, however, these robots should be able to walk on different terrains, such as soil, sand, grass, and so on, without losing balance, getting stuck or falling over.
To teach an AI agent a new task, like how to open a kitchen cabinet, researchers often use reinforcement learning—a trial-and-error process where the agent is rewarded for taking actions that get it closer to the goal.
Drawing inspiration from birds, fish and even worms, researchers in Europe are developing machines to explore places on Earth that are difficult for people to reach.
ETH Zurich researchers deployed an autonomous excavator, called HEAP, to build a 6-meter-high and 65-meter-long dry-stone wall. The wall is embedded in a digitally planned and autonomously excavated landscape and park.
It isn't easy for a robot to find its way out of a maze. Picture these machines trying to traverse a kid's playroom to reach the kitchen, with miscellaneous toys scattered across the floor and furniture blocking some potential paths. This messy labyrinth requires the robot to calculate the most optimal journey to its destination, without crashing into any obstacles. What is the bot to do?
In recent years, roboticists have introduced increasingly advanced systems, which could open exciting new possibilities for surgery, rehabilitation, and health care assistance. These robotic systems are already helping to improve the quality of life of many people with disabilities, as well as patients who suffered physical trauma or underwent medical procedures.
With 3D inkjet printing systems, engineers can fabricate hybrid structures that have soft and rigid components, like robotic grippers that are strong enough to grasp heavy objects but soft enough to interact safely with humans.
While most robots are initially tested in laboratory settings and other controlled environments, they are designed to be deployed in real-world environments, helping humans to tackle various problems. Navigating real-world environments entails dealing with high levels of uncertainty and unpredictability, particularly when robots are completing missions as a team.
According to data from 2010, around 1.8 million people in the U.S. can't eat on their own. Yet training a robot to feed people presents an array of challenges for researchers. Foods come in a nearly endless variety of shapes and states (liquid, solid, gelatinous), and each person has a unique set of needs and preferences.
Interest in the incorporation of robots into security, policing and military operations has been steadily increasing over the last few years. It's an avenue already being explored in both North America and Europe.
Cambridge engineers investigating the load-bearing capacity of conical shells, made from soft materials, have discovered performance-limiting weaknesses that could have implications for soft robotics—affecting the ability of morphing cones to perform fundamental mechanical tasks.