Sumo: Dynamic and Generalizable Whole-Body Loco-Manipulation

2026-04-09

AI Summary

This work addresses the limited whole-body coordination and generalization capability of legged robots when dynamically manipulating large, heavy objects in real-world environments. The authors propose a framework that integrates a pretrained whole-body control policy with a sampling-based online planner. By adapting the cost function at test time, the approach generalizes across diverse objects and tasks without requiring additional training, enabling flexible adjustment of behavioral objectives. The method is demonstrated on a Spot robot performing dynamic manipulation of oversized and overweight objects—such as uprighting heavy tires and dragging large barriers—for the first time. It is further validated in simulation on a humanoid robot executing tasks like opening doors and pushing tables, confirming its effectiveness and broad applicability.

Abstract

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 \href{https://sumo.rai-inst.com/}{https://sumo.rai-inst.com/}.

Problem

• loco-manipulation
• legged robots
• whole-body control
• dynamic manipulation
• sim-to-real

Innovation

• sim-to-real
• whole-body control
• loco-manipulation
• sample-based planning
• zero-shot generalization