SENSE: A Force-Sensor-Free, Model-Based Framework for Estimating External Interaction Forces on Humanoid Robots

This study introduces SENSE, an innovative sensorless external force estimation framework for humanoid robots, built on a model-driven approach. Unlike conventional methods that depend on the simplified Linear Inverted Pendulum (LIP) model and offline data processing, SENSE enables real-time and online estimation. By leveraging centroidal dynamics and integrating angular momentum, our approach accurately infers external forces without the need for Force/Torque (F/T) sensors. The proposed method is validated in simulation using the NAO humanoid robot model within the qiBullet physics engine, under both static standing and realistic dynamic walking conditions. Robustness is evaluated using two external force profiles: constant and time-varying (e.g., sinusoidal). Results show that SENSE provides accurate and stable force estimation, even in challenging scenarios such as foot contact transitions where low-cost sensors like Force-Sensing Resistors (FSRs) typically become unreliable. With its low computational cost and reliance only on onboard sensors, SENSE offers a practical alternative to expensive F/T sensors for medium-sized humanoid platforms. To promote reproducibility and further research, the full implementation is publicly available.