  This paper introduces the Stoch BiRo, a cost-effective bipedal robot designed
with a modular mechanical structure having point feet to navigate uneven and
unfamiliar terrains. The robot employs proprioceptive actuation in abduction,
hips, and knees, leveraging a Raspberry Pi4 for control. Overcoming
computational limitations, a Learning-based Linear Policy controller manages
balance and locomotion with only 3 degrees of freedom (DoF) per leg, distinct
from the typical 5DoF in bipedal systems. Integrated within a modular control
architecture, these controllers enable autonomous handling of unforeseen
terrain disturbances without external sensors or prior environment knowledge.
The robot's policies are trained and simulated using MuJoCo, transferring
learned behaviors to the Stoch BiRo hardware for initial walking validations.
This work highlights the Stoch BiRo's adaptability and cost-effectiveness in
mechanical design, control strategies, and autonomous navigation, promising
diverse applications in real-world robotics scenarios.
