Sim-to-real approach for dynamic whole-body loco-manipulation with legged robots. Uses test-time steering of pre-trained whole-body control policies with sample-based planning. Activation: whole-body control, loco-manipulation, legged robots, sim-to-real.
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/.
The proposed approach consists of several key components:
This work builds upon and extends recent advances in:
@article{2604_08508,
title={Sumo: Dynamic and Generalizable Whole-Body Loco-Manipulation},
author={John Z. Zhang and Maks Sorokin and Jan Brüdigam and et al.},
journal={arXiv preprint arXiv:2604.08508v1},
year={2026}
}
execreadwriteUser: 请帮我应用此技能
Agent: 我将按照标准流程执行...
User: 有更复杂的场景需要处理
Agent: 针对复杂场景，我将采用以下策略...