Movement is how people interact with and affect their environment. For
realistic character animation, it is necessary to synthesize such interactions
between virtual characters and their surroundings. Despite recent progress in
character animation using machine learning, most systems focus on controlling
an agent's movements in fairly simple and homogeneous environments, with
limited interactions with other objects. Furthermore, many previous approaches
that synthesize human-scene interactions require significant manual labeling of
the training data. In contrast, we present a system that uses adversarial
imitation learning and reinforcement learning to train physically-simulated
characters that perform scene interaction tasks in a natural and life-like
manner. Our method learns scene interaction behaviors from large unstructured
motion datasets, without manual annotation of the motion data. These scene
interactions are learned using an adversarial discriminator that evaluates the
realism of a motion within the context of a scene. The key novelty involves
conditioning both the discriminator and the policy networks on scene context.
We demonstrate the effectiveness of our approach through three challenging
scene interaction tasks: carrying, sitting, and lying down, which require
coordination of a character's movements in relation to objects in the
environment. Our policies learn to seamlessly transition between different
behaviors like idling, walking, and sitting. By randomizing the properties of
the objects and their placements during training, our method is able to
generalize beyond the objects and scenarios depicted in the training dataset,
producing natural character-scene interactions for a wide variety of object
shapes and placements. The approach takes physics-based character motion
generation a step closer to broad applicability.

本文提出了一种使用对抗性模仿学习和强化学习来训练物理模拟角色表现场景交互任务的系统，无需手动注释大型非结构化运动数据，并且在训练过程中随机对象属性和其放置位置，从而能够推广到训练数据集中未呈现的物体和场景，并以自然的角色场景交互方式进行表现。