Synthesizing graceful and life-like behaviors for physically simulated
characters has been a fundamental challenge in computer animation. Data-driven
methods that leverage motion tracking are a prominent class of techniques for
producing high fidelity motions for a wide range of behaviors. However, the
effectiveness of these tracking-based methods often hinges on carefully
designed objective functions, and when applied to large and diverse motion
datasets, these methods require significant additional machinery to select the
appropriate motion for the character to track in a given scenario. In this
work, we propose to obviate the need to manually design imitation objectives
and mechanisms for motion selection by utilizing a fully automated approach
based on adversarial imitation learning. High-level task objectives that the
character should perform can be specified by relatively simple reward
functions, while the low-level style of the character's behaviors can be
specified by a dataset of unstructured motion clips, without any explicit clip
selection or sequencing. These motion clips are used to train an adversarial
motion prior, which specifies style-rewards for training the character through
reinforcement learning (RL). The adversarial RL procedure automatically selects
which motion to perform, dynamically interpolating and generalizing from the
dataset. Our system produces high-quality motions that are comparable to those
achieved by state-of-the-art tracking-based techniques, while also being able
to easily accommodate large datasets of unstructured motion clips. Composition
of disparate skills emerges automatically from the motion prior, without
requiring a high-level motion planner or other task-specific annotations of the
motion clips. We demonstrate the effectiveness of our framework on a diverse
cast of complex simulated characters and a challenging suite of motor control
tasks.

利用对抗性模仿学习的方法自动选择带有无结构动作片段数据集的物理模拟角色的行为，无需手动设计仿制目标和运动选择机制。该方法在训练对抗性运动先验的同时也自动选择要执行的运动，并能够轻松地适应大型的无结构运动片段数据集，且不需要高级运动规划器或其他任务特定的运动片段注释。在一系列的模拟案例和挑战性的运动控制任务中，我们展示了其有效性。

AMP：针对基于物理的角色控制的对抗运动先验的风格化方法

AMP: Adversarial Motion Priors for Stylized Physics-Based Character  Control

A longstanding goal in character animation is to combine data-driven
specification of behavior with a system that can execute a similar behavior in
a physical simulation, thus enabling realistic responses to perturbations and
environmental variation. We show that well-known reinforcement learning (RL)
methods can be adapted to learn robust control policies capable of imitating a
broad range of example motion clips, while also learning complex recoveries,
adapting to changes in morphology, and accomplishing user-specified goals. Our
method handles keyframed motions, highly-dynamic actions such as
motion-captured flips and spins, and retargeted motions. By combining a
motion-imitation objective with a task objective, we can train characters that
react intelligently in interactive settings, e.g., by walking in a desired
direction or throwing a ball at a user-specified target. This approach thus
combines the convenience and motion quality of using motion clips to define the
desired style and appearance, with the flexibility and generality afforded by
RL methods and physics-based animation. We further explore a number of methods
for integrating multiple clips into the learning process to develop
multi-skilled agents capable of performing a rich repertoire of diverse skills.
We demonstrate results using multiple characters (human, Atlas robot, bipedal
dinosaur, dragon) and a large variety of skills, including locomotion,
acrobatics, and martial arts.

本研究旨在将数据驱动的行为规范与能够在物理模拟中执行类似行为的系统相结合，以实现对扰动和环境变化的逼真响应。通过将运动模仿目标与任务目标相结合，我们可以训练出能够在交互环境中智能反应的角色，例如向所需方向行走或向用户指定目标扔球。同时，本研究探讨了多种集成多个运动片段的方法，以开发出能够执行丰富多样的技能的多才多艺的代理。