ベイズ外乱注入：ロボットマニピュレーションのための柔軟な方策のロバスト模倣学習
Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies for Robot Manipulation
人間の実演に見られる複数の最適行動やリスクに応じた回復行動を模倣するため、ベイズ推論で柔軟な非パラメトリック多行動方策を学習しつつ、リスク感受性のある外乱を注入して方策をロバスト化する模倣学習フレームワークBDIを提案した。
著者: Hanbit Oh, Hikaru Sasaki, Brendan Michael, Takamitsu Matsubara
分類: cs.RO
原文アブストラクト
Humans demonstrate a variety of interesting behavioral characteristics when performing tasks, such as selecting between seemingly equivalent optimal actions, performing recovery actions when deviating from the optimal trajectory, or moderating actions in response to sensed risks. However, imitation learning, which attempts to teach robots to perform these same tasks from observations of human demonstrations, often fails to capture such behavior. Specifically, commonly used learning algorithms embody inherent contradictions between the learning assumptions (e.g., single optimal action) and actual human behavior (e.g., multiple optimal actions), thereby limiting robot generalizability, applicability, and demonstration feasibility. To address this, this paper proposes designing imitation learning algorithms with a focus on utilizing human behavioral characteristics, thereby embodying principles for capturing and exploiting actual demonstrator behavioral characteristics. This paper presents the first imitation learning framework, Bayesian Disturbance Injection (BDI), that typifies human behavioral characteristics by incorporating model flexibility, robustification, and risk sensitivity. Bayesian inference is used to learn flexible non-parametric multi-action policies, while simultaneously robustifying policies by injecting risk-sensitive disturbances to induce human recovery action and ensuring demonstration feasibility. Our method is evaluated through risk-sensitive simulations and real-robot experiments (e.g., table-sweep task, shaft-reach task and shaft-insertion task) using the UR5e 6-DOF robotic arm, to demonstrate the improved characterisation of behavior. Results show significant improvement in task performance, through improved flexibility, robustness as well as demonstration feasibility.
