モデル予測制御による人間共生ロボットの対人制御ポリシー生成
Exploring Model Predictive Control to Generate Optimal Control Policies for HRI Dynamical Systems
人間とロボットの相互作用を線形力学系としてモデル化し、混合整数制約付きモデル予測制御で人間に配慮した制御ポリシーを生成。支援ロボットとアイコンタクト制御するヒューマノイドで検証した。
著者: Steven Jens Jorgensen, Orion Campbell, Travis Llado, Donghyun Kim, Junhyeok Ahn, Luis Sentis
分類: cs.HC, cs.RO
原文アブストラクト
We model Human-Robot-Interaction (HRI) scenarios as linear dynamical systems and use Model Predictive Control (MPC) with mixed integer constraints to generate human-aware control policies. We motivate the approach by presenting two scenarios. The first involves an assistive robot that aims to maximize productivity while minimizing the human's workload, and the second involves a listening humanoid robot that manages its eye contact behavior to maximize "connection" and minimize social "awkwardness" with the human during the interaction. Our simulation results show that the robot generates useful behaviors as it finds control policies to minimize the specified cost function. Further, we implement the second scenario on a humanoid robot and test the eye contact scenario with 48 human participants to demonstrate and evaluate the desired controller behavior. The humanoid generated 25% more eye contact when it was told to maximize connection over when it was told to maximize awkwardness. However, despite showing the desired behavior, there was no statistical difference between the participant's perceived connection with the humanoid.
