A Unified Control Framework Enables Robust Robotic Haptic Rendering of Bimanual Rehabilitation Tasks

The haptic rendering function of bimanual rehabilitation robots is critical in promoting motor learning and neural plasticity. However, achieving high transparency and robust haptic rendering for diverse tasks remains challenging. The reason is that tasks with varying dynamic characteristics require distinct control architectures to maintain a balance between rendering accuracy and stability. To address this issue, we propose a unified control framework capable of adapting to various tasks. In this framework, tasks are classified into four types based on their dynamic characteristics. Aligned with the task type, the control architecture is reconstructed by invoking and integrating different encapsulated blocks, where each block functions as a subsystem with unique capabilities. To verify the proposed framework, a bimanual rehabilitation robotic system was developed and experimentally validated on eight human participants. Results indicate that the average force errors are less than 1 N for non-load moving tasks, and for tasks with a load of 20 N, the average force rendering accuracy exceeds 94%. The results confirm that the framework is capable of reaching a favorable compromise between stability and accuracy for various bimanual training tasks.