ロボット観測データのバイアスを除去する因果推論に基づく運動推定
Causal Inference for De-biasing Motion Estimation from Robotic Observational Data
ロボットの観測データに含まれるバイアスを因果推論（IPWとDR）で補正し、確率的運動モデルのパラメータを精度よく推定する枠組みを提案した。
著者: Junhong Xu, Kai Yin, Jason M. Gregory, Lantao Liu
分類: cs.RO
原文アブストラクト
Robot data collected in complex real-world scenarios are often biased due to safety concerns, human preferences, and mission or platform constraints. Consequently, robot learning from such observational data poses great challenges for accurate parameter estimation. We propose a principled causal inference framework for robots to learn the parameters of a stochastic motion model using observational data. Specifically, we leverage the de-biasing functionality of the potential-outcome causal inference framework, the Inverse Propensity Weighting (IPW), and the Doubly Robust (DR) methods, to obtain a better parameter estimation of the robot's stochastic motion model. The IPW is a re-weighting approach to ensure unbiased estimation, and the DR approach further combines any two estimators to strengthen the unbiased result even if one of these estimators is biased. We then develop an approximate policy iteration algorithm using the bias-eliminated estimated state transition function. We validate our framework using both simulation and real-world experiments, and the results have revealed that the proposed causal inference-based navigation and control framework can correctly and efficiently learn the parameters from biased observational data.
