论文标题
稀疏的表示学习，经过修改的Q-VAE，以最小化世界模型的实现
Sparse Representation Learning with Modified q-VAE towards Minimal Realization of World Model
论文作者
论文摘要
从高维观测数据中提取低维潜在空间对于在提取的潜在空间上构建具有世界模型的实时机器人控制器至关重要。但是，没有建立的方法可以自动调整潜在空间的尺寸，因为它发现了必要和足够的尺寸大小，即世界模型的最小实现。在这项研究中，我们分析并改善了基于Tsallis的变分自动编码器（Q-VAE），并揭示，在适当的配置下，它总是有助于使潜在空间稀疏。即使与最小的实现相比，预先指定的潜在空间的尺寸是多余的，这种稀疏也会崩溃不必要的尺寸，从而易于清除它们。我们通过提出的方法在实验中验证了稀疏的好处，它可以通过需要六维状态空间的移动操纵器来轻松找到达到到达任务的必要和足够的六个维度。此外，通过在提取的维度中学到的最低实现世界模型的计划，该提出的方法能够实时发挥最佳的动作序列，从而将达到的成就时间降低了约20％。随附的视频已上传到YouTube：https：//youtu.be/-qjitrnxars上
Extraction of low-dimensional latent space from high-dimensional observation data is essential to construct a real-time robot controller with a world model on the extracted latent space. However, there is no established method for tuning the dimension size of the latent space automatically, suffering from finding the necessary and sufficient dimension size, i.e. the minimal realization of the world model. In this study, we analyze and improve Tsallis-based variational autoencoder (q-VAE), and reveal that, under an appropriate configuration, it always facilitates making the latent space sparse. Even if the dimension size of the pre-specified latent space is redundant compared to the minimal realization, this sparsification collapses unnecessary dimensions, allowing for easy removal of them. We experimentally verified the benefits of the sparsification by the proposed method that it can easily find the necessary and sufficient six dimensions for a reaching task with a mobile manipulator that requires a six-dimensional state space. Moreover, by planning with such a minimal-realization world model learned in the extracted dimensions, the proposed method was able to exert a more optimal action sequence in real-time, reducing the reaching accomplishment time by around 20 %. The attached video is uploaded on youtube: https://youtu.be/-QjITrnxaRs
