The industrial sector has demonstrated increased interest in implementing in-hand manipulation as an alternative to conventional parallel gripper hands, enabling dexterous object handling with substantial pose changes. In-hand manipulation involves challenges such as complex contact transitions with robot fingers and difficult-to-model factors such as friction and surface irregularities, which complicate real-world applications despite successful simulations. This study proposes a novel approach for in-hand manipulation that outperforms traditional model-based motion-generation methods. A motion teaching system utilizing a leader–follower system was developed to address the limitations of conventional methods. This technique leverages human skills for robot motion teaching without requiring motion programming or precise modeling. The control system ensures stability during motion teaching, adapts to objects and operators with unknown dynamics, and can easily be extended to a motion-reproduction system. For motion-trajectory generation, we introduced a method that identifies highly reproducible motion trajectories by analyzing the similarity of time-series data from multiple teaching datasets. By selecting from the teaching data, we determine a motion trajectory that maintains a force consistent with the motion over time. Experiments validated the efficacy of the proposed system.

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