Virginia Tech Cuts Soft-Robot Control Power 75-Fold With Reservoir Computing
Updated
Updated · Robotics and Automation News · May 27
Virginia Tech Cuts Soft-Robot Control Power 75-Fold With Reservoir Computing
1 articles · Updated · Robotics and Automation News · May 27
Virginia Tech researchers used reservoir computing to control a simulated soft robotic arm that flexes, twists, warps and bends, calling it the first method able to handle this type of fast-moving, highly flexible arm.
The approach beat conventional AI and machine-learning control methods, and running the reservoir on a neuromorphic chip reduced power use by up to 75 times.
The team modeled a snake-like arm with an elastic core and paired synthetic muscles, then fed virtual movement trials back into the neural reservoir to learn how the muscles interact.
Published in PNAS, the work could support smaller untethered robots for medicine, agriculture, salvage and infrastructure inspection; the next step is building physical prototypes.
With robots nearing octopus-like skill, are we prepared for the challenges of their real-world deployment?
Can a virtual robot's brain survive the leap from simulation to the unpredictable physical world?
Powering the Future: Virginia Tech’s Reservoir Computing Breakthrough Makes Soft Robots Smarter and More Efficient (2026)
Overview
In 2026, Virginia Tech announced a major breakthrough in soft robotics by introducing a new control method that uses reservoir computing to manage the complex movements of flexible, muscle-like robots. This innovation addresses a long-standing challenge in the field and is expected to greatly reduce the power needed for soft robot operation. As a result, soft robots are becoming more practical and efficient for real-world applications. The research team, led by Noel Naughton, built comprehensive data sets for virtual robotic arms, which are essential for understanding and predicting the intricate movements of these advanced machines.