Researchers at King's College London have determined how to provide robots with complex instructions without using electricity, potentially freeing up more space in the robotic “brain” for “thinking.” The research was published in the journal Advanced Science.
The controller valve and soft robotic hopper which can move without electricity. Image Credit: King’s College London
Researchers developed an innovative compact circuit that mimics certain functions of the human body by transmitting a series of commands to devices using pressure variations within a fluid inside it.
This could pave the way for a new breed of robots whose bodies could function without the assistance of an internal control center, and more sophisticated AI-powered software might instead be able to utilize this space.
Delegating tasks to different parts of the body frees up computational space for robots to ‘think,’ allowing future generations of robots to be more aware of their social context or even more dexterous. This opens the door for a new kind of robotics in places like social care and manufacturing.
Dr. Antonio Forte, Senior Lecturer and Study Senior Author, King’s College London
The study revealed that it may also pave the way for the development of robots that can function in environments inaccessible to electricity-powered devices, such as MRI rooms and areas exposed to radiation like Chornobyl, which destroys circuits.
The researchers anticipate that low-income nations without consistent access to energy may one day be able to employ these robots.
Put simply, robots are split into two parts: the brain and the body. An AI brain can help run the traffic system of a city, but many robots still struggle to open a door – why is that? Software has advanced rapidly in recent years, but hardware has not kept up. By creating a hardware system independent from the software running it, we can offload a lot of the computational load onto the hardware, in the same way, your brain does not need to tell your heart to beat.
Dr. Antonio Forte, Senior Lecturer and Study Senior Author, King’s College London
Presently, computer chips and electricity are necessary for the operation of any robot. An encoder on a robotic “brain” of software and algorithms transmits data to the hardware or body so that it can execute a task.
This is especially problematic in “soft robotics,” a science that uses soft materials to produce devices like robotic muscles. It introduces hard electronic encoders and strains the software to allow the material to move sophisticatedly, like grasping a door handle.
To overcome this, the researchers created a reconfigurable circuit with an adjustable valve to be installed in a robot's hardware. This valve, which functions similarly to a transistor in a typical circuit, allows engineers to replicate binary code and convey messages straight to hardware through pressure. This enables the robot to carry out intricate maneuvers without the need for power or brain commands, permitting more control than the existing fluid-based circuits.
The innovative circuit frees up computational capacity for future robotic systems to be more complex, adaptive, and useful by shifting the workload from the software to the hardware.
The researchers' next goal is to scale up their circuits from experimental pipettes and hoppers to larger robots, such as wheeled robots with fully soft engines or crawlers used to monitor power plants.
Ultimately, without investment in embodied intelligence robots will plateau. Soon, if we do not offload the computational load that modern-day robots take on, algorithmic improvements will have little impact on their performance. Our work is just a first step on this path, but the future holds smarter robots with smarter bodies.
Mostafa Mousa, Post-Graduate Researcher and Study Author, King’s College London
Frequency Controlled Fluidic Oscillators for Soft Robots
Video Credit: King’s College London
Journal Reference:
Mousa, M., et al. (2024) Frequency‐Controlled Fluidic Oscillators for Soft Robots. Advanced Science. doi.org/10.1002/advs.202408879.