# Sumo: Dynamic and Generalizable Whole-Body Loco-Manipulation

arXiv: https://arxiv.org/abs/2604.08508 · HTML: https://mlsanity.com/paper/2604.08508/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2026.

## Built on by (most cited first, 3 of 3)

- [HOLO-MPPI: Multi-Scenario Motion Planning via Hierarchical Policy Optimization](https://mlsanity.com/paper/2606.16480/) (2026)
- [Learning Multi-Humanoid Pickup and Transport via Decentralized Object-Centric Control](https://mlsanity.com/paper/2609.17824/) (2026)
- [InterEvolve: Test-Time Evolution of Reward Programs for Humanoid Loco-Manipulation](https://mlsanity.com/paper/2610.02196/) (2026)

## Abstract (arXiv)

This paper presents a sim-to-real approach that enables legged robots to dynamically manipulate large and heavy objects with whole-body dexterity. Our key insight is that by performing test-time steering of a pre-trained whole-body control policy with a sample-based planner, we can enable these robots to solve a variety of dynamic loco-manipulation tasks. Interestingly, we find our method generalizes to a diverse set of objects and tasks with no additional tuning or training, and can be further enhanced by flexibly adjusting the cost function at test time. We demonstrate the capabilities of our approach through a variety of challenging loco-manipulation tasks on a Spot quadruped robot in the real world, including uprighting a tire heavier than the robot's nominal lifting capacity and dragging a crowd-control barrier larger and taller than the robot itself. Additionally, we show that the same approach can be generalized to humanoid loco-manipulation tasks, such as opening a door and pushing a table, in simulation. Project code and videos are available at https://sumo.rai-inst.com/.