Multi-objective optimization problems, which require the simultaneous optimization of multiple terms, are prevalent across numerous applications. Existing multi-objective optimization methods often rely on manually tuned aggregation functions to formulate a joint optimization target. The performance of such hand-tuned methods is heavily dependent on careful weight selection, a time-consuming and laborious process. These limitations also arise in the setting of reinforcement-learning-based motion tracking for physically simulated characters, where intricately crafted reward functions are typically used to achieve high-fidelity results. Such solutions not only require domain expertise and significant manual adjustment, but also limit the applicability of the resulting reward function across diverse skills. To bridge this gap, we present a novel adversarial multi-objective optimization technique that is broadly applicable to a range of multi-objective optimization problems, including motion tracking. The proposed adversarial differential discriminator receives a single positive sample, yet is still effective at guiding the optimization process. We demonstrate that our technique can enable characters to closely replicate a variety of acrobatic and agile behaviors, achieving comparable quality to state-of-the-art motion-tracking methods, without relying on manually tuned reward functions. Results are best visualized through https://youtu.be/rz8BYCE9E2w.

本研究解决了多目标优化中手动调优聚合函数的效率低下和适用性局限的问题。我们提出了一种新的对抗性多目标优化技术，它通过对抗性差分判别器有效引导优化过程，能够使角色实现丰富的杂技和灵活行为，且质量与最先进的运动跟踪方法相当，无需依赖手动调优的奖励函数，从而扩展了相关领域的应用潜力。

基于物理的运动模仿：对抗性差分判别器的应用