Full‐Body Optimization‐Based Bipedal Walking Control With Task‐Space Inverse Dynamics and Virtual Constraints

Achieving stable and versatile bipedal locomotion remains a major challenge in robotics, with applications in personal assistance, healthcare, and search and rescue. The hybrid zero dynamics (HZD) framework, based on virtual constraints, has shown strong potential for generating provably stable gaits. However, traditional HZD implementations often rely on simple feedback controllers that lack the ability to strictly enforce physical constraints, such as actuator limits and ground contact conditions during real‐time execution. This paper presents a full‐body, optimization‐based walking controller for a bipedal robot that integrates virtual‐constraints‐based gait planning with online tracking using task‐space inverse dynamics (TSID). A gait library is generated offline using two different sets of virtual constraints. It is shown that constraints based on the center of mass (CoM) relative to the feet improve optimization performance, achieving 8% fewer iterations, 12% faster convergence, and an 11.6% better objective value compared to classical constraint sets. The TSID controller, formulated as a weighted quadratic program (WQP), enables simultaneous tracking of multiple task objectives while respecting dynamic consistency and physical constraints. Simulation results on the GR‐1 humanoid robot demonstrate stable walking over a range of velocities, smooth transitions between walking speeds, and successful rejection of external disturbances up to 20 N·s, confirming the effectiveness and robustness of the proposed approach.