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Peking University team proposes motion prediction method for flexible robotic arms, reducing long-range error by more than a quarter

A team led by Xie Guangming and Zhang Feitian at Peking University proposed a hybrid model that uses an equilibrium anchoring model and a lightweight correction model to suppress the accumulation of step-by-step prediction errors in flexible robotic arms. The work was published at RSS 2026. When predicting 200 steps (6 seconds) on a simulated 3D cable-driven robotic arm, the hybrid model's error was more than a quarter lower than that of the anchoring model alone, and none of 19 tests showed loss of accuracy. On a physical hydraulic soft robotic fish tail, its error was half or less that of the anchoring model and a quarter or less that of the correction model. More stable predictions could let a controller first try out multiple actions in a computer and pick the safest one.

Part of the event: Peking University team proposes hybrid model for flexible arm motion prediction

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