A hierarchical whole-body control framework for humanoid robots in shelf-picking

Purpose This paper aims to present a hierarchical whole-body control (H-WBC) framework for humanoid shelf-picking in structured shelf environments. The objective is to improve motion coordination, posture regulation and safe task execution for humanoid manipulation in spatially constrained workspaces. Design/methodology/approach The proposed framework combines hierarchical whole-body kinematics, a unified task-constraint formulation and a strict-priority hierarchical quadratic programming scheme. End-effector tracking, waist posture regulation, arm-motion regularization, postural-stability constraints, joint-motion limits and shelf-related collision avoidance are integrated into a common velocity-level optimization framework. The method is evaluated in simulation and on the UBTECH Walker2 humanoid robot through single-arm and dual-arm shelf-picking tasks. Findings The proposed framework achieves accurate end-effector motion and coordinated whole-body behavior in constrained shelf environments. In simulation, it maintains low tracking errors in representative trajectory-following tasks, preserves postural stability and improves task success while reducing redundant arm motion in dual-arm shelf-picking across different shelf heights. In real-world experiments, it demonstrates practical execution of both single-arm pick-and-place and dual-arm box retrieval tasks, while maintaining stable and collision-aware whole-body motion near the shelf structure. Originality/value This study develops a reusable H-WBC framework tailored to humanoid shelf-picking. The proposed formulation unifies task objectives and physical constraints for shelf-picking within a strict-priority optimization hierarchy, and is validated in simulation and real-robot experiments on representative single-arm and dual-arm tasks in structured storage environments.