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