# Hierarchical Optimization-based Control for Whole-body Loco-manipulation of Heavy Objects

arXiv: https://arxiv.org/abs/2311.00112 · HTML: https://mlsanity.com/paper/2311.00112/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2023.

## Built on by (most cited first, 6 of 6)

- [GentleHumanoid: Learning Upper-body Compliance for Contact-rich Human and Object Interaction](https://mlsanity.com/paper/2511.04679/) (2025)
- [Hierarchical Contact-Rich Trajectory Optimization for Multi-Modal Manipulation using Tight Convex Relaxations](https://mlsanity.com/paper/2503.07963/) (2025)
- [CAIMAN: Causal Action Influence Detection for Sample-efficient Loco-manipulation](https://mlsanity.com/paper/2502.00835/) (2025)
- [Scoop-and-Toss: Dynamic Object Collection for Quadrupedal Systems](https://mlsanity.com/paper/2506.09406/) (2025)
- [From Perception to Assistance: Open-Vocabulary Shared Autonomy for Robotic Manipulation](https://mlsanity.com/paper/2607.17323/) (2026)
- [Learning Loco-Manipulation From SMPC Demonstrations With Sparse Offline-to-Online RL](https://mlsanity.com/paper/2608.12063/) (2026)

## Abstract (arXiv)

In recent years, the field of legged robotics has seen growing interest in enhancing the capabilities of these robots through the integration of articulated robotic arms. However, achieving successful loco-manipulation, especially involving interaction with heavy objects, is far from straightforward, as object manipulation can introduce substantial disturbances that impact the robot's locomotion. This paper presents a novel framework for legged loco-manipulation that considers whole-body coordination through a hierarchical optimization-based control framework. First, an online manipulation planner computes the manipulation forces and manipulated object task-based reference trajectory. Then, pose optimization aligns the robot's trajectory with kinematic constraints. The resultant robot reference trajectory is executed via a linear MPC controller incorporating the desired manipulation forces into its prediction model. Our approach has been validated in simulation and hardware experiments, highlighting the necessity of whole-body optimization compared to the baseline locomotion MPC when interacting with heavy objects. Experimental results with Unitree Aliengo, equipped with a custom-made robotic arm, showcase its ability to lift and carry an 8kg payload and manipulate doors.