Quantum Enhanced Transformer Network for Learning Transactive Energy During Physical Human-Robot Interaction

Optimizing energy transfer during physical human–robot interactions is important for enhancing neurotherapeutic outcomes and ensuring patient safety. Energy transfer dynamics are particularly complex, involving a delicate balance between kinetic and potential energies as the robot assists or resists movement, adapting to the patient’s needs in real time. Traditional methods, which often rely on predefined robot control strategies, often struggle in dynamic environments where the interplay of forces and motions becomes unpredictable. Therefore, this work integrates the computational intelligence of quantum computing with transformer models to estimate the dynamics of energy transfer between human and gait rehabilitation robot, specifically designed based on the Stephenson III six-bar linkage mechanism. The principles of quantum computing, such as superposition and entanglement, combined with the attention mechanisms of transformer models, explore a much larger solution space. It provides accurate predictions of the complex, nonlinear interactions of energy flows between the robot and the human lower limb. The quantum transformer network was trained on the experimental data obtained from the interaction of seven male and one female healthy human subjects with the gait rehabilitation robot operated at low and high impedance control modes.