Bipedal Robot Navigation

Bipedal robot navigation research focuses on enabling robots to safely and smoothly traverse complex environments, particularly those shared with humans. Current efforts concentrate on integrating path planning and gait generation, often using model predictive control (MPC) frameworks coupled with neural networks (e.g., zonotope-based networks) to predict pedestrian behavior and ensure collision avoidance while adhering to the robot's dynamic constraints. This work leverages techniques like Control Barrier Functions and signal temporal logic to guarantee safety and social acceptability, addressing the challenges of navigating both cluttered and human-populated spaces. These advancements are crucial for deploying bipedal robots in real-world scenarios, improving their robustness and reliability in dynamic environments.