Sumo：动态且可泛化的全身体姿操控
Sumo: Dynamic and Generalizable Whole-Body Loco-Manipulation
AI总结：
本文提出一种仿真到现实的方法，使四足机器人能够动态操控大重物。通过测试时调整预训练的全身体控制策略，实现多种动态体姿操控任务，且无需额外训练即可泛化到不同物体和任务。
摘要：
本文提出了一种仿真到现实的方法，使四足机器人能够动态操控大且重的物体。我们的关键见解是通过在测试时调整预训练的全身体控制策略，结合基于样本的规划器，使机器人能够解决多种动态体姿操控任务。有趣的是，我们的方法无需额外调优或训练即可泛化到多样化的物体和任务，并可通过灵活调整测试时的成本函数进一步增强。我们通过在现实世界中Spot四足机器人上执行各种具有挑战性的体姿操控任务来展示方法的能力，包括支撑比机器人名义举重能力更大的轮胎以及拖动比机器人自身更大更高的路障。此外，我们还展示了相同方法可以泛化到人形机器人的体姿操控任务，如开门和推桌子。项目代码和视频可在https://sumo.rai-inst.com/上获得。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
