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.
This paper reviews the concept of Functional Safety as it relates to machinery. The design steps for a safe machine are outlined and the methodology for determining appropriate PL/SIL safety ratings discussed.
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.
A mantra often heard within the manufacturing and logistics industries is that robots are not taking the jobs of humans. And, in many ways, this is true. Today, automation is even a necessary solution to plug a shortage of human labour.
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?
The benefits demonstrated by this project underscore the transformative potential of automation in advancing solar construction practices, enabling us to accelerate and de-risk our project pipeline
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.
The easy-to-integrate system consists of a module for robot arms, a computing unit with pre-installed intelligent software and a camera module, each equipped in series with two uEye+ XCP cameras from IDS.
The international media event at Thunderhill Raceway, Willows, CA, marked a first for TRI, offering journalists from the US and Europe an opportunity to ride inside its research vehicles and simulators and experience firsthand how TRI is approaching autonomy.