Flexible robot arms learn to predict their next move
Flexible robot arms learn to predict their next move
Researchers from Peking University have developed a method to improve the accuracy of motion predictions for flexible robot arms, using a hybrid model that combines an anchor and correction model. This approach reduces prediction errors significantly in both simulated and real-world scenarios, enabling better control and planning for flexible robotic systems.
The study introduces a hybrid model combining an anchor and correction model to improve prediction accuracy for flexible robot arms, showing significant error reduction in both simulation and real-world tests.
Source: Tech Xplore — Robotics · Read original article ↗
Article text · Original source · English
A robot arm that bends like an elephant's trunk can slip into tight spaces and handle fragile objects. Controlling it is harder because its smooth bending makes its next move hard to predict. Researchers have now developed a method that keeps computer predictions of the arm's motion on track, even when the arm moves faster than the model was trained for.
The study comes from a team involving professors Xie Guangming and Zhang Feitian at Peking University's School of Advanced Manufacturing and Robotics. It was presented at Robotics: Science and Systems (RSS 2026), which was held in Sydney from July 13 to 17, 2026.
It is now published in the conference proceedings Robotics: Science and Systems XXII, and details of the project are available on the project website.
Before a flexible robot moves, its controller often rehearses possible commands in a computer model and picks the best one. Models trained on data predict one small step at a time, and each step starts from the last, so small errors pile up. After 200 steps, the forecast can be far off, and the robot can be working from a false picture of its own body.
Key findings
The team's idea is to give the forecast a reference point. One model, the anchor, learns the shape the arm settles into whenever its controls are held steady. At every step, it pulls the prediction back toward that shape, so errors cannot snowball. But the arm swings and bends on its way to rest, so a second, lightweight model, the correction, learns those dynamics from the arm's motion data. Together they form a hybrid model.
The team first tested the idea in simulation, on a three-dimensional arm driven by cables. Asked to predict 6 seconds of motion, or 200 steps, well outside what it was trained on, the hybrid model cut its error by more than a quarter compared with the anchor alone. It also beat the correction alone and never lost track in any of the 19 tests.
Then came a real machine: a hydraulic soft fishlike tail that swings from side to side when pressurized with water. Here the hybrid model's error was half or less that of the anchor alone, and a quarter or less that of the correction alone. On standard benchmark tests, it also finished every prediction, while a competing method sometimes failed.
Future implications
Steadier predictions give a robot's controller something it can trust. It could try out many possible moves on the computer first and choose the safest one before the real arm moves. A robot that can foresee how its own body will bend can plan a careful move before it makes one. That could help flexible arms work in places rigid machines struggle to reach, such as tight spaces, and with objects that break easily.
More information
Ahsan Tanveer et al., Stabilizing 3D Continuum-Arm Rollouts via Equilibrium Anchoring and Feature-Lifted Residual Learning, Robotics: Science and Systems XXII (2026). DOI: 10.15607/rss.2026.xxii.106
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Citation: Flexible robot arms learn to predict their next move (2026, October 23) retrieved 23 October 2026 from https://techxplore.com/news/2026-10-flexible-robot-arms.html
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dney from July 13 to 17, 2026. It is now published in the conference proceedings Robotics: Science and Systems XXII , and details of the project are available on the project website . Before a flexible robot moves, its controller often rehearses possible commands in a computer model and picks the best one. Models trained on data predict one small step at a time, and each step starts from the last, so small errors pil
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to rest, so a second, lightweight model, the correction, learns those dynamics from the arm's motion data. Together they form a hybrid model. The team first tested the idea in simulation, on a three-dimensional arm driven by cables. Asked to predict 6 seconds of motion, or 200 steps, well outside what it was trained on, the hybrid model cut its error by more than a quarter compared with the anchor alone. It also bea
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t the correction alone and never lost track in any of the 19 tests. Then came a real machine: a hydraulic soft fishlike tail that swings from side to side when pressurized with water. Here the hybrid model's error was half or less that of the anchor alone, and a quarter or less that of the correction alone. On standard benchmark tests, it also finished every prediction, while a competing method sometimes failed. A so
Open source S7
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