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Autonomously Learning One-To-Many Social Interaction Logic from Human-Human Interaction Data (CROSBI ID 705836)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

Nanavati, Amal ; Doering, Malcolm ; Brščić, Dražen ; Kanda, Takayuki Autonomously Learning One-To-Many Social Interaction Logic from Human-Human Interaction Data // HRI '20: Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction. The Association for Computing Machinery (ACM), 2020. str. 419-427 doi: 10.1145/3319502.3374798

Podaci o odgovornosti

Nanavati, Amal ; Doering, Malcolm ; Brščić, Dražen ; Kanda, Takayuki

engleski

Autonomously Learning One-To-Many Social Interaction Logic from Human-Human Interaction Data

We envision a future where service robots autonomously learn how to interact with humans directly from human-human interaction data, without any manual intervention. In this paper, we present a data-driven pipeline that: (1) takes in low-level data of a human shopkeeper interacting with multiple customers (28 hours of collected data) ; (2) autonomously extracts high-level actions from that data ; and (3) learns -- without manual intervention -- how a robotic shopkeeper should respond to customers' actions online. Our proposed system for learning the interaction logic uses neural networks to first learn which customer actions are important to respond to and then learn how the shopkeeper should respond to those important customer actions. We present a novel technique for learning which customer actions are important by first learning the hidden causal relationship between customer and shopkeeper actions. In an offline evaluation, we show that our proposed technique significantly outperforms state-of-the-art baselines, in both which customer actions are important and how to respond to them.

Learning from demonstrations ; HCI theory, concepts and models

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Podaci o prilogu

419-427.

2020.

objavljeno

10.1145/3319502.3374798

Podaci o matičnoj publikaciji

HRI '20: Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

The Association for Computing Machinery (ACM)

978-1-4503-6746-2

Podaci o skupu

ACM/IEEE International Conference on Human-Robot Interaction

predavanje

23.03.2020-26.03.2020

Cambridge, Ujedinjeno Kraljevstvo

Povezanost rada

Računarstvo

Poveznice