A Deep Learning Model to Endow Social Robots with Addressee Estimation Skills

Core Concepts

A deep learning model can endow social robots with the ability to estimate the addressee of a speaker's utterance by interpreting the speaker's non-verbal bodily cues.

Abstract

This paper presents a deep learning model for addressee estimation, which is the ability to understand the addressee of a speaker's utterance. The model takes two visual inputs - the speaker's face images and body pose vectors - and uses a hybrid CNN-LSTM architecture to classify the addressee's position relative to the speaker (left, right, or the robot).
The key highlights and insights are:

The model was designed to be deployable on social robots and work in ecological interaction scenarios, using only data from the robot's own sensors.
Experiments show that combining face and body pose information in an intermediate fusion approach leads to better performance compared to using a single modality or a late fusion approach.
The model can provide reliable addressee predictions even before the utterance is complete, with performance improving as more of the utterance is observed.
Compared to prior work on binary addressee classification, the proposed three-class model provides more detailed information about the addressee's position.
The model outperforms a state-of-the-art binary addressee classification model on the Vernissage dataset.

Overall, the work demonstrates how deep learning can be used to endow social robots with addressee estimation skills, which is an important capability for natural and effective human-robot interaction.

Stats

The model was trained and tested using the Vernissage dataset, which contains multimodal recordings of interactions between two humans and a Nao robot.

Quotes

"Communicating means sharing, might it be a message, a thought, or an inner state, and is an act that inherently shapes the social world."
"To properly be part of the social environment, each agent needs to understand some basic dynamics of communication, such as to whom a message is directed."

Key Insights Distilled From

To Whom are You Talking? A Deep Learning Model to Endow Social Robots with Addressee Estimation Skills

by Carlo Mazzol... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2308.10757.pdf

Deeper Inquiries

How could the model be extended to handle scenarios with more than two human participants?

To extend the model to handle scenarios with more than two human participants, the model's architecture would need to be adjusted to accommodate additional addressee positions. One approach could be to modify the classification task to include all possible addressee positions in the environment, such as "LEFT", "RIGHT", "ROBOT", "GROUP", and potentially more positions depending on the scenario. This would require retraining the model with the new classes and ensuring that the input data and features capture the interactions between multiple participants accurately. Additionally, the temporal aspect of the task would become more complex as the model would need to track interactions between multiple speakers and addressees over time.

How could the model's addressee predictions be used to enhance other social skills and capabilities of the robot, such as turn-taking, role detection, or grounding of deictic references?

The model's addressee predictions can be leveraged to enhance various social skills and capabilities of the robot. For example, by accurately identifying the addressee of an utterance, the robot can improve its turn-taking abilities by knowing when to respond or when to yield the floor to another participant. Additionally, the model's predictions can aid in role detection by understanding the social dynamics and roles in multiparty interactions. This information can help the robot adapt its behavior and responses based on the roles of the participants in the interaction. Furthermore, the addressee predictions can assist in grounding deictic references, such as understanding pronouns like "you," "he," "she," or "they" in the context of the conversation. By knowing the addressee, the robot can correctly interpret and respond to these references, enhancing the overall naturalness and effectiveness of the interaction.

What other non-verbal cues, beyond face and body pose, could be incorporated to further improve addressee estimation performance?

Incorporating additional non-verbal cues beyond face and body pose can further improve addressee estimation performance. Some potential cues to consider include:

Gaze Direction: Analyzing the direction of the speaker's gaze can provide valuable information about their focus of attention and the intended addressee.
Hand Gestures: Observing the speaker's hand gestures can offer insights into their communicative intentions and help identify the target of their message.
Head Movements: Monitoring the speaker's head movements, such as nods or shakes, can indicate agreement, disagreement, or emphasis on certain points, aiding in addressee estimation.
Facial Expressions: Analyzing the speaker's facial expressions can provide emotional context to their speech and help determine the addressee based on the emotional content of the message.
Proximity: Considering the physical distance between the speaker and potential addressees can also be a useful cue, as individuals tend to address those who are closer to them spatially.
By integrating these additional non-verbal cues into the model, it can gain a more comprehensive understanding of the social dynamics and interactions, leading to more accurate addressee estimation performance.