状態推定のための深層正規化フロー
Deep Normalizing Flows for State Estimation
正規化フローと表現力の高い深層ニューラルネットワークを組み合わせてエージェントの真の状態に対する信念を学習し、ロボットの状態推定タスクで従来手法を上回る性能を示した論文。
著者: Harrison Delecki, Liam A. Kruse, Marc R. Schlichting, Mykel J. Kochenderfer
分類: cs.RO
原文アブストラクト
Safe and reliable state estimation techniques are a critical component of next-generation robotic systems. Agents in such systems must be able to reason about the intentions and trajectories of other agents for safe and efficient motion planning. However, classical state estimation techniques such as Gaussian filters often lack the expressive power to represent complex underlying distributions, especially if the system dynamics are highly nonlinear or if the interaction outcomes are multi-modal. In this work, we use normalizing flows to learn an expressive representation of the belief over an agent's true state. Furthermore, we improve upon existing architectures for normalizing flows by using more expressive deep neural network architectures to parameterize the flow. We evaluate our method on two robotic state estimation tasks and show that our approach outperforms both classical and modern deep learning-based state estimation baselines.
