面向人形机器人箱子移动操作的仿真到现实学习
Sim-to-Real Learning for Humanoid Box Loco-Manipulation
AI总结：
本研究针对人形机器人箱子移动操作需全身协调并保持平衡的难题，提出仿真到现实的强化学习方法，为Digit机器人训练拾取搬运技能，整合系统实现多规格箱子的桌间转移，并验证了仿真到现实的成功迁移。
摘要：
本研究提出了一种基于学习的人形机器人箱子移动操作方法。该问题极具挑战性，因为需要全身协调以举起不同重量、位置和朝向的箱子，同时保持平衡。为应对这一挑战，我们提出了一种仿真到现实的强化学习方法，用于为双足机器人Digit训练通用的箱子拾取与搬运技能。我们设计的奖励函数旨在实现与箱子的预期交互，同时重视平衡和步态质量。我们将习得的技能整合到一个完整的箱子移动操作系统中，以完成将不同尺寸、重量和初始配置的箱子从一张桌子移动到另一张桌子的任务。除了定量仿真结果外，我们还在人形机器人上展示了成功的仿真到现实迁移。
英文摘要：
In this work we propose a learning-based approach to box loco-manipulation for a humanoid robot. This is a particularly challenging problem due to the need for whole-body coordination in order to lift boxes of varying weight, position, and orientation while maintaining balance. To address this challenge, we present a sim-to-real reinforcement learning approach for training general box pickup and carrying skills for the bipedal robot Digit. Our reward functions are designed to produce the desired interactions with the box while also valuing balance and gait quality. We combine the learned skills into a full system for box loco-manipulation to achieve the task of moving boxes from one table to another with a variety of sizes, weights, and initial configurations. In addition to quantitative simulation results, we demonstrate successful sim-to-real transfer on the humanoid r
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