動的圧縮を伴う変分潜在空間でのベイズ最適化
Bayesian Optimization in Variational Latent Spaces with Dynamic Compression
シミュレーション軌道を教師なしで低次元潜在空間に埋め込み、不要な状態空間の探索を動的に圧縮することで、10〜20回の試行でロボットの制御器を最適化するベイズ最適化手法を提案した。
著者: Rika Antonova, Akshara Rai, Tianyu Li, Danica Kragic
分類: cs.RO, cs.LG
原文アブストラクト
Data-efficiency is crucial for autonomous robots to adapt to new tasks and environments. In this work we focus on robotics problems with a budget of only 10-20 trials. This is a very challenging setting even for data-efficient approaches like Bayesian optimization (BO), especially when optimizing higher-dimensional controllers. Simulated trajectories can be used to construct informed kernels for BO. However, previous work employed supervised ways of extracting low-dimensional features for these. We propose a model and architecture for a sequential variational autoencoder that embeds the space of simulated trajectories into a lower-dimensional space of latent paths in an unsupervised way. We further compress the search space for BO by reducing exploration in parts of the state space that are undesirable, without requiring explicit constraints on controller parameters. We validate our approach with hardware experiments on a Daisy hexapod robot and an ABB Yumi manipulator. We also present simulation experiments with further comparisons to several baselines on Daisy and two manipulators. Our experiments indicate the proposed trajectory-based kernel with dynamic compression can offer ultra data-efficient optimization.
