CASPER: Cognitive Architecture for Social Perception and Engagement in Robots

VINANZI, Samuele and CANGELOSI, Angelo (2024). CASPER: Cognitive Architecture for Social Perception and Engagement in Robots. International Journal of Social Robotics.

Vinanzi-CASPERCognitiveArchitecture(VoR).pdf - Published Version
Creative Commons Attribution.

Download (3MB) | Preview
Official URL:
Open Access URL: (Published version)
Link to published version::


Our world is being increasingly pervaded by intelligent robots with varying degrees of autonomy. To seamlessly integrate themselves in our society, these machines should possess the ability to navigate the complexities of our daily routines even in the absence of a human's direct input. In other words, we want these robots to understand the intentions of their partners with the purpose of predicting the best way to help them. In this paper, we present the initial iteration of CASPER (Cognitive Architecture for Social Perception and Engagement in Robots): a symbolic cognitive architecture that uses qualitative spatial reasoning to anticipate the pursued goal of another agent and to calculate the best collaborative behavior. This is performed through an ensemble of parallel processes that model a low-level action recognition and a high-level goal understanding, both of which are formally verified. We have tested this architecture in a simulated kitchen environment and the results we have collected show that the robot is able to both recognize an ongoing goal and to properly collaborate towards its achievement. This demonstrates a new use of Qualitative Spatial Relations applied to the problem of intention reading in the domain of human-robot interaction.

Item Type: Article
Uncontrolled Keywords: 0801 Artificial Intelligence and Image Processing; 0915 Interdisciplinary Engineering; 2002 Cultural Studies; 4602 Artificial intelligence; 4608 Human-centred computing
Identification Number:
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 29 Feb 2024 13:47
Last Modified: 14 Mar 2024 15:15

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics