# CASPER: Cognitive Architecture for Social Perception and Engagement in Robots

arXiv: https://arxiv.org/abs/2209.01012 · HTML: https://mlsanity.com/paper/2209.01012/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 74 references that are in the map.
- First posted on arXiv 1 Sep 2022.

## Builds on (most cited first, 5 of 5)

- [Connecting Gaze, Scene, and Attention: Generalized Attention Estimation via Joint Modeling of Gaze and Scene Saliency](https://mlsanity.com/paper/1807.10437/) (2018)
- [Human-robot collaboration and machine learning: a systematic review of recent research](https://mlsanity.com/paper/2110.07448/) (2021)
- [Action Anticipation: Reading the Intentions of Humans and Robots](https://mlsanity.com/paper/1802.02788/) (2018)
- [Intent-aware Multi-agent Reinforcement Learning](https://mlsanity.com/paper/1803.02018/) (2018)
- [Combining Deep Learning and Qualitative Spatial Reasoning to Learn Complex Structures from Sparse Examples with Noise](https://mlsanity.com/paper/1811.11064/) (2018)

## Built on by (most cited first, 1 of 1)

- [Towards a cognitive architecture to enable natural language interaction in co-constructive task learning](https://mlsanity.com/paper/2503.23760/) (2025)

## Abstract (arXiv)

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 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.