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

翻译：摘要：本文提出了一种基于学习的方法，用于人形机器人的箱子搬运操作。由于需要全身协调以抬起重量、位置和方向各异的箱子，同时保持平衡，这一问题极具挑战性。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，用于训练双足机器人Digit的通用箱体拾取和搬运技能。我们的奖励函数设计旨在产生期望的箱子交互行为，同时重视平衡能力和步态质量。我们将学习到的技能整合为一个完整的箱子搬运操作系统，以实现将不同尺寸、重量和初始配置的箱子从一个桌子移动到另一个桌子的任务。除定量仿真结果外，我们还在人形机器人上成功展示了仿真到现实的迁移。