Humans perform everyday tasks using a combination of locomotion and manipulation skills. Building a system that can handle both skills is essential to creating virtual humans. We present a physically-simulated human capable of solving box rearrangement tasks, which requires a combination of both skills. We propose a hierarchical control architecture, where each level solves the task at a different level of abstraction, and the result is a physics-based simulated virtual human capable of rearranging boxes in a cluttered environment. The control architecture integrates a planner, diffusion models, and physics-based motion imitation of sparse motion clips using deep reinforcement learning. Boxes can vary in size, weight, shape, and placement height. Code and trained control policies are provided.

翻译：人类通过结合移动与操控技能完成日常任务。构建兼具这两种能力的系统对创建虚拟人类至关重要。本文提出一种物理仿真人体模型，能够解决需要两者协同的箱体重排任务。我们设计了一种分层控制架构，其中每个层级以不同抽象程度处理任务，最终生成可在杂乱环境中重新排列箱体的物理仿真虚拟人类。该控制架构集成了规划器、扩散模型，以及基于深度强化学习对稀疏运动片段进行物理运动模仿的技术。箱体的尺寸、重量、形状和放置高度均可变化。本文提供相关代码及训练好的控制策略。