Bipedal Walking

Bipedal walking research focuses on enabling robots to walk stably and efficiently, mimicking human locomotion. Current efforts concentrate on developing robust controllers using model predictive control (MPC), diffusion models, and reinforcement learning (RL), often incorporating neural networks like those predicting ground reaction forces to bridge the simulation-to-reality gap. These advancements aim to improve robot agility across diverse terrains, including challenging environments with limited footholds, and enhance the reliability and adaptability of bipedal locomotion in real-world applications such as search and rescue or assistive technologies. Furthermore, research explores bio-inspired approaches, analyzing human gait to inform robot design and control strategies.