Domain-adaptive trajectory imitation is a skill that some predators learn for
survival, by mapping dynamic information from one domain (their speed and
steering direction) to a different domain (current position of the moving
prey). An intelligent agent with this skill could be exploited for a diversity
of tasks, including the recognition of abnormal motion in traffic once it has
learned to imitate representative trajectories. Towards this direction, we
propose DATI, a deep reinforcement learning agent designed for domain-adaptive
trajectory imitation using a cycle-consistent generative adversarial method.
Our experiments on a variety of synthetic families of reference trajectories
show that DATI outperforms baseline methods for imitation learning and optimal
control in this setting, keeping the same per-task hyperparameters. Its
generalization to a real-world scenario is shown through the discovery of
abnormal motion patterns in maritime traffic, opening the door for the use of
deep reinforcement learning methods for spatially-unconstrained trajectory data
mining.

本研究提出了 DATI，一个使用循环一致的生成对抗方法设计的迁移轨迹模仿的深度强化学习代理，在多个合成轨迹模式的实验中表现优于基线模型和最优控制，可以用于异常运动识别和空间不受限的轨迹数据挖掘。

通过领域自适应模仿学习动态系统的代表性轨迹

Learning Representative Trajectories of Dynamical Systems via  Domain-Adaptive Imitation

Learning agile skills is one of the main challenges in robotics. To this end,
reinforcement learning approaches have achieved impressive results. These
methods require explicit task information in terms of a reward function or an
expert that can be queried in simulation to provide a target control output,
which limits their applicability. In this work, we propose a generative
adversarial method for inferring reward functions from partial and potentially
physically incompatible demonstrations for successful skill acquirement where
reference or expert demonstrations are not easily accessible. Moreover, we show
that by using a Wasserstein GAN formulation and transitions from demonstrations
with rough and partial information as input, we are able to extract policies
that are robust and capable of imitating demonstrated behaviors. Finally, the
obtained skills such as a backflip are tested on an agile quadruped robot
called Solo 8 and present faithful replication of hand-held human
demonstrations.

提出一种基于生成对抗学习的方法，通过从部分不兼容的演示中推断奖励函数，实现成功的技能获取，并在四足机器人上测试了后空翻等技能。

通过对粗略局部演示进行对抗性模仿来学习敏捷技能

Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations

This paper presents a novel framework for generating texture mosaics with
convolutional neural networks. Our method is called GANosaic and performs
optimization in the latent noise space of a generative texture model, which
allows the transformation of a content image into a mosaic exhibiting the
visual properties of the underlying texture manifold. To represent that
manifold, we use a state-of-the-art generative adversarial method for texture
synthesis, which can learn expressive texture representations from data and
produce mosaic images with very high resolution. This fully convolutional model
generates smooth (without any visible borders) mosaic images which morph and
blend different textures locally. In addition, we develop a new type of
differentiable statistical regularization appropriate for optimization over the
prior noise space of the PSGAN model.

本文提出了一种名为 GANosaic 的基于卷积神经网络的纹理马赛克生成框架，该框架可以将内容图像转换为具有所需纹理特性的马赛克图像。该方法利用了一种基于生成对抗方法的纹理合成技术来表示纹理 manifold 并生成平滑的无可见边界的马赛克图像，并且该方法添加了适用于 PSGAN 模型先验噪声空间优化的新型可微统计正则化。