Hand-Guided Human–Robot Interaction With a Learning-Based Controller for Soft Robot Arms

This work presents a novel control strategy for the hand-guidance of a soft continuum arm. This promotes human-robot interaction in collaborative robotics; the proposed control system is implemented on a modular soft manipulator, exploiting its intrinsic safety. During hand guidance, users exert forces on the robot’s end effector, which dynamically generates a smooth path for the robot to follow. A learning-based model is employed to predict the actuation patterns required to achieve desired positions, effectively solving the robot’s inverse dynamics. The system’s performance was evaluated under four distinct scenarios. In the human-guidance usability test, the controller demonstrated an average position error of 7.73 ± 0.96 mm. To validate robustness across different users, an inter-subject variability test with 10 participants yielded a consistent average error of 6.24 ± 3.05 mm. The system’s resilience was further assessed in a noisy environment test, where the error remained bounded between 6.99 ± 3.40 mm and 34.64 ± 14.75 mm even under severe sensor degradation. Finally, under motor-driven guidance, the controller ensured accuracy across varying movement directions with an average position error of 3.82±3.32 mm. These results underline the potential of soft robots for safe and interactive use in everyday environments, showcasing their suitability for collaborative applications.