Optimal Gait

Optimal gait research aims to understand and replicate the most efficient and stable locomotion patterns in robots and animals, focusing on minimizing energy consumption and maximizing speed and robustness. Current research employs diverse approaches, including model predictive control (MPC), reinforcement learning (RL), and various bio-inspired models like the spring-loaded inverted pendulum (SLIP) and linear inverted pendulum (LIP), often incorporating hierarchical control architectures and deep learning for gait segmentation and generation. These advancements have implications for improving the performance of legged robots in diverse environments, as well as providing insights into the fundamental principles governing animal locomotion and potentially informing the design of assistive devices for humans.