With the growing demand for using biped robots in industrial automation and other related applications, navigation and path planning has emerged as one of the most challenging research topics over the last few decades. In this paper, a novel navigational controller is designed and implemented in a self-fabricated biped robot. After fabricating biped robots equipped with a large set of sensors, a regression controller is implemented on them for the purposes of obstacle avoidance and path optimization. The obstacle distances detected by the biped's sensory network are fed as input parameters to the regression controller, and the output obtained from the controller is the necessary heading angle required to avoid the obstacles present randomly in the environment. The biped is tested in a simulated environment for obstacle avoidance and target-following behavior. Further, to validate the simulation results, a real-time experimental setup is designed under laboratory conditions. The results obtained from both environments are compared in terms of navigational parameters and, then, good agreement is observed between them. Being a relatively new area of research, the navigation of bipeds can pave the way towards industrial automation. © 2020 Sharif University of Technology. All rights reserved.