# Humanoid Hanoi: Investigating Shared Whole-Body Control for Skill-Based Box Rearrangement

arXiv: https://arxiv.org/abs/2602.13850 · HTML: https://mlsanity.com/paper/2602.13850/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2026.

## Built on by (most cited first, 2 of 2)

- [MEMENTO: Memory-Guided Memetic Code-as-Policy Evolution](https://mlsanity.com/paper/2607.22832/) (2026)
- [CHOREO: Every Humanoid Skill as a Trajectory](https://mlsanity.com/paper/2609.22274/) (2026)

## Abstract (arXiv)

We introduce Humanoid Hanoi, a challenge problem for long-horizon humanoid skill composition that requires a robot to repeatedly sequence pickup, locomotion, and placement skills to rearrange stacked boxes in simulation and on hardware. This symbolically simple task exposes a weakness in a common modular humanoid architecture, where reusable high-level skills are learned on top of a shared, task-agnostic whole-body controller (WBC). While such a WBC provides a unified low-level interface for skill composition, it may not be sufficiently hardened to execute the full collection of learned skills under domain randomization and long-horizon closed-loop execution. We compare strategies for addressing this issue, including skill-specific WBC fine-tuning, residual low-level adaptation, and task-driven data aggregation that expands the shared-WBC training distribution with closed-loop trajectories induced by learned skills while preserving its original tracking objective. In our evaluated system, the shared-WBC approach achieves the strongest long-horizon robustness. A direct transition-smoothing intervention further shows that low-level controller handoffs account for a substantial part of the composition loss observed with non-shared controllers. Project page: https://osudrl.github.io/HumanoidHanoi/