The Engineer – AI and computer simulations help exoskeletons take the strain
The novel method is said to rapidly develop exoskeleton controllers to assist locomotion without relying on lengthy human-involved experiments. Detailed in Nature, the method can apply to a variety of assistive devices beyond the hip exoskeleton demonstrated in this research.
“It can also apply to knee or ankle exoskeletons, or other multi-joint exoskeletons,” said Xianlian Zhou, associate professor and director of the New Jersey Institute of Technology’s BioDynamics Lab. In addition, it can similarly be applied to above-the-knee or below-the-knee prosthesis, he said in a statement.
“Our approach marks a significant advancement in wearable robotics, as our exoskeleton controller is exclusively developed through AI-driven simulations,” said Zhou.
According to NJIT, this breakthrough holds promise for aiding individuals with mobility challenges, including the elderly or stroke survivors, without necessitating their presence in a laboratory or clinical setting for extensive testing.
“This work proposes and demonstrates a new method that uses physics-informed and data-driven reinforcement learning to control wearable robots in order to directly benefit humans,” said Hao Su, corresponding author of a paper on the work and an associate professor of mechanical and aerospace engineering at North Carolina State University.
Exoskeletons have potential to improve human locomotive performance across a wide variety of users, from injury rehabilitation to permanent assistance for people with disabilities. However, lengthy human tests and control laws have limited its broad adoption.
The researchers focused on improving autonomous control of embodied AI systems that are integrated into a physical technology.
This work focused on teaching robotic exoskeletons how to assist able-bodied people with a variety of movements, and expands on previous reinforcement learning based research for lower limb rehabilitation exoskeletons.
“Previous achievements in reinforcement learning have tended to focus primarily on simulation and board games,” said Shuzhen Luo, assistant professor at Embry-Riddle Aeronautical University and the first author of both works. “Our method provides a foundation for turnkey solutions in controller development for wearable robots.”
Normally, users must spend hours ‘training’ an exoskeleton so that the technology knows how much force is needed – and when to apply that force – to help users walk, run or climb stairs.
The new method allows users to utilise the exoskeletons immediately because the closed-loop simulation incorporates both exoskeleton controller and physics models of musculoskeletal dynamics, human-robot interaction, and muscle reactions, thereby generating efficient and realistic data and iteratively learning better control policy in simulation.
The unit is pre-programmed to be ready to use right away, and it is also possible to update the controller on the hardware if researchers make improvements in the lab through expanded simulations. Future prospects for this project include developing individualised, custom-tailored controllers that assist users for various activities of daily living.
“This work is essentially making science fiction reality – allowing people to burn less energy while conducting a variety of tasks,” said Su.
In testing with human subjects, the researchers found that study participants used 24.3 per cent less metabolic energy when walking in the robotic exoskeleton, compared to walking without the exoskeleton. Participants used 13.1 per cent less energy when running in the exoskeleton, and 15.4 per cent less energy when climbing stairs.
While this study focused on the researchers’ work with able-bodied people, the new method aims to help people with mobility impairments using assistive devices.
“Our framework may offer a generalisable and scalable strategy for the rapid development and widespread adoption of a variety of assistive robots for both able-bodied and mobility-impaired individuals,” said Su.
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