The emerging behaviors of swarms have fascinated scientists and gathered significant interest in the field of robotics. Traditionally, swarms are viewed as egalitarian, with robots sharing identical roles and capabilities. However, recent findings highlight the importance of hierarchy for deploying robot swarms more effectively in diverse scenarios. Despite nature's preference for hierarchies, the robotics field has clung to the egalitarian model, partly due to a lack of empirical evidence for the conditions favoring hierarchies. Our research demonstrates that while egalitarian swarms excel in environments proportionate to their collective sensing abilities, they struggle in larger or more complex settings. Hierarchical swarms, conversely, extend their sensing reach efficiently, proving successful in larger, more unstructured environments with fewer resources. We validated these concepts through simulations and physical robot experiments, using a complex radiation cleanup task. This study paves the way for developing adaptable, hierarchical swarm systems applicable in areas like planetary exploration and autonomous vehicles. Moreover, these insights could deepen our understanding of hierarchical structures in biological organisms.

We present a method for the control of robot swarms using two subsets of robots: a larger group of simple, oblivious robots (which we call the workers) that is governed by simple local attraction forces, and a smaller group (the guides) with sufficient mission knowledge to create and displace a desired worker formation by operating on the local forces of the workers. The guides coordinate to shape the workers like smart particles by changing their interaction parameters. We study the approach with a large scale experiment in a physics based simulator with up to 5000 robots forming three different patterns. Our experiments reveal that the approach scales well with increasing robot numbers, and presents little pattern distortion. We evaluate the approach on a physical swarm of robots that use visual inertial odometry to compute their relative positions and obtain results that are comparable with simulation. This work lays the foundation for designing and coordinating configurable smart particles, with applications in smart materials and nanomedicine.

Computer scientists at the University of Leeds are using the artificial intelligence (AI) techniques of automated planning and reinforcement learning to "train" a robot to find an object in a cluttered space, such as a warehouse shelf or in a fridge -- and move it. The aim is to develop robotic autonomy, so the machine can assess the unique circumstances presented in a task and find a solution -- akin to a robot transferring skills and knowledge to a new problem. The Leeds researchers are presenting their findings today (Monday, November 4) at the International Conference on Intelligent Robotics and Systems in Macau, China. The big challenge is that in a confined area, a robotic arm may not be able to grasp an object from above. Instead it has to plan a sequence of moves to reach the target object, perhaps by manipulating other items out of the way.

Practise makes perfect – it is an adage that has helped humans become highly dexterous and now it is an approach that is being applied to robots. Computer scientists at the University of Leeds are using the artificial intelligence (AI) techniques of automated planning and reinforcement learning to "train" a robot to find an object in a cluttered space, such as a warehouse shelf or in a fridge – and move it. The aim is to develop robotic autonomy, so the machine can assess the unique circumstances presented in a task and find a solution – akin to a robot transferring skills and knowledge to a new problem. The Leeds researchers are presenting their findings today (Monday, November 4) at the International Conference on Intelligent Robotics and Systems in Macau, China. The big challenge is that in a confined area, a robotic arm may not be able to grasp an object from above.

Many organizations have ambitious plans to scale up the automation of their IT and business processes. Having seen encouraging returns on their initial deployments, they are keen to increase the rewards and increase their use of automation. We are already hearing of organizations that have deployed many hundreds of software robots in their enterprises. All of these robots need to be optimized, managed, and maintained efficiently to make sure they run well and maximize the return on investment. By the time you do that for the thousands of robots running in your organization, you will need robots to manage the robots.

Three weeks ago we reported an amusing anecdote out of China in which robot waiters in a Guangzhou restaurant had been "fired" because whencustomers flocked to the Heweilai Restaurant chain in the southern Chinese city, they found they were not all they are cracked up to be. "A staff member said the robots couldn't effectively handle soup dishes, often malfunctioned, and had to follow a fixed route that sometimes resulted in clashes. A customer also said the robots were unable to do tasks such as topping up water or placing a dish on the table." "The robots weren't able to carry soup or other food steady and they would frequently break down. The boss has decided never to use them again," said one employee. We joked in the summary saying that "for now, it appears, China's minimum wage workers, and it has a few hundred million of those, will not be phased out just yet." According to a report released by the MIT Technology Review, where some saw failure in China's "novelty" worker robots, the Chinese government saw nothing less than the opportunity to perfect what will soon put million of Chinese workers out of a job: an army of worker robots. Because while there is certanly humor to be found in the anecdote about a robot "termination", the Chinese government is keen to change this.