Human Intent Estimation in pHRi 

 In physical human-robot interaction (pHRI), achieving high transparency is crucial for ensuring that a robot accurately follows human motion intentions, especially in free-space conditions. Transparency is often compromised by interaction forces that arise from dynamic mismatches between the human and the robot. To address this, a control framework has been developed that combines admittance control with a discrete energy-based compensator. This approach uses time delay control to compute real-time compensation for dynamic inconsistencies, allowing the robot to respond more naturally to user input. By minimizing the energy generated by interaction forces, the method enhances transparency without requiring additional hardware or complex modeling. The approach is particularly beneficial in scenarios where compliant motion and low resistance are essential, such as teaching by demonstration or cooperative manipulation. This work emphasizes the value of dynamic interaction modeling in pHRi and offers a practical solution to improve human-robot collaboration through better alignment with human intent.

Human Impedance Compensation

 A compensator for physical human-robot interaction has been developed using the SPSA and AMSGrad optimization algorithms. The overall algorithm is based on admittance control. Unlike the traditional method, which estimated the impedance of a human hand through simple experiments, this improved method employs optimization for impedance compensation. Experiments were conducted using a 2-axis hydraulic robot.