# Self-Supervised Prediction of the Intention to Interact with a Service Robot

arXiv: https://arxiv.org/abs/2309.07477 · HTML: https://mlsanity.com/paper/2309.07477/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2023.

## Built on by (most cited first, 7 of 7)

- [Predicting the Intention to Interact with a Service Robot:the Role of Gaze Cues](https://mlsanity.com/paper/2404.01986/) (2024)
- [Learning from Implicit User Feedback, Emotions and Demographic Information in Task-Oriented and Document-Grounded Dialogues](https://mlsanity.com/paper/2401.09248/) (2024)
- [MINT-RVAE: Multi-Cues Intention Prediction of Human-Robot Interaction using Human Pose and Emotion Information from RGB-only Camera Data](https://mlsanity.com/paper/2509.22573/) (2025)
- [Real-Time Human-Robot Interaction Intent Detection Using RGB-based Pose and Emotion Cues with Cross-Camera Model Generalization](https://mlsanity.com/paper/2512.17958/) (2025)
- [HUI360: A 360deg Egocentric Dataset and Baselines for Human-Robot Interaction Anticipation](https://mlsanity.com/paper/2608.11051/) (2026)
- [Interact with me: Joint Egocentric Forecasting of Intent to Interact, Attitude and Social Actions](https://mlsanity.com/paper/2412.16698/) (2024)
- [Sixth-Sense: Self-Supervised Learning of Spatial Awareness of Humans from a Planar Lidar](https://mlsanity.com/paper/2502.21029/) (2025)

## Abstract (arXiv)

A service robot can provide a smoother interaction experience if it has the ability to proactively detect whether a nearby user intends to interact, in order to adapt its behavior e.g. by explicitly showing that it is available to provide a service. In this work, we propose a learning-based approach to predict the probability that a human user will interact with a robot before the interaction actually begins; the approach is self-supervised because after each encounter with a human, the robot can automatically label it depending on whether it resulted in an interaction or not. We explore different classification approaches, using different sets of features considering the pose and the motion of the user. We validate and deploy the approach in three scenarios. The first collects 3442 natural sequences (both interacting and non-interacting) representing employees in an office break area: a real-world, challenging setting, where we consider a coffee machine in place of a service robot. The other two scenarios represent researchers interacting with service robots (200 and 72 sequences, respectively). Results show that, even in challenging real-world settings, our approach can learn without external supervision, and can achieve accurate classification (i.e. AUROC greater than 0.9) of the user's intention to interact with an advance of more than 3s before the interaction actually occurs.