物体状態・ロボット知識・人間の（誤）信念の統合推論
Joint Inference of States, Robot Knowledge, and Human (False-)Beliefs
物体状態・ロボット知識・人間の（誤）信念をグラフィカルモデルで統一的に表現し、複数視点のパースグラフを統合推論することで人間の信念認識と物体追跡精度を向上させた研究。
著者: Tao Yuan, Hangxin Liu, Lifeng Fan, Zilong Zheng, Tao Gao, Yixin Zhu, Song-Chun Zhu
分類: cs.CV, cs.RO
原文アブストラクト
Aiming to understand how human (false-)belief--a core socio-cognitive ability--would affect human interactions with robots, this paper proposes to adopt a graphical model to unify the representation of object states, robot knowledge, and human (false-)beliefs. Specifically, a parse graph (pg) is learned from a single-view spatiotemporal parsing by aggregating various object states along the time; such a learned representation is accumulated as the robot's knowledge. An inference algorithm is derived to fuse individual pg from all robots across multi-views into a joint pg, which affords more effective reasoning and inference capability to overcome the errors originated from a single view. In the experiments, through the joint inference over pg-s, the system correctly recognizes human (false-)belief in various settings and achieves better cross-view accuracy on a challenging small object tracking dataset.
