Data Driven Computational Model for Bipedal Walking and Push Recovery

TL;DR

This paper presents a data-driven hybrid computational model for bipedal walking and push recovery, aiming to improve robot stability and adaptability by mimicking human-like responses to external perturbations.

Contribution

The research introduces a novel hybrid system framework for modeling human-like bipedal locomotion and push recovery using data-driven methods, overcoming limitations of traditional kinematic models.

Findings

Developed an adaptable hybrid model for bipedal walking.

Analyzed push recovery strategies and their variability among humans.

Proposed a learning-based approach for improved push negotiation in robots.

Abstract

In this research, we have developed the data driven computational walking model to overcome the problem with traditional kinematics based model. Our model is adaptable and can adjust the parameter morphological similar to human. The human walk is a combination of different discrete sub-phases with their continuous dynamics. Any system which exhibits the discrete switching logic and continuous dynamics can be represented using a hybrid system. In this research, the bipedal locomotion is analyzed which is important for understanding the stability and to negotiate with the external perturbations. We have also studied the other important behavior push recovery. The Push recovery is also a very important behavior acquired by human with continuous interaction with environment. The researchers are trying to develop robots that must have the capability of push recovery to safely maneuver in a dynamic environment. The push is a very commonly experienced phenomenon in cluttered environment. The human beings can recover from external push up to a certain extent using different strategies of hip, knee and ankle. The different human beings have different push recovery capabilities. For example a wrestler has a better push negotiation capability compared to normal human beings. The push negotiation capability acquired by human, therefore, is based on learning but the learning mechanism is still unknown to researchers. The research community across the world is trying to develop various humanoid models to solve this mystery. Seeing all the conventional mechanics and control based models have some inherent limitations, a learning based computational model has been developed to address effectively this issue. In this research we will discuss how we have framed this problem as hybrid system.