# Multiperspective Teaching of Unknown Objects via Shared-gaze-based Multimodal Human-Robot Interaction

## TL;DR

This paper introduces a multimodal human-robot interaction method using shared gaze and augmented reality to teach robots new objects, enabling quick learning from few examples and detection of previously unknown classes.

## Contribution

It presents a novel pipeline combining gaze and augmented reality for human-guided robot object teaching, with a publicly available dataset for further research.

## Key findings

- Robots can learn new objects from few instances using the proposed method.
- The approach enables detection of classes without prior training data.
- Different machine learning models and training data sizes influence detection performance.

## Abstract

For successful deployment of robots in multifaceted situations, an understanding of the robot for its environment is indispensable. With advancing performance of state-of-the-art object detectors, the capability of robots to detect objects within their interaction domain is also enhancing. However, it binds the robot to a few trained classes and prevents it from adapting to unfamiliar surroundings beyond predefined scenarios. In such scenarios, humans could assist robots amidst the overwhelming number of interaction entities and impart the requisite expertise by acting as teachers. We propose a novel pipeline that effectively harnesses human gaze and augmented reality in a human-robot collaboration context to teach a robot novel objects in its surrounding environment. By intertwining gaze (to guide the robot's attention to an object of interest) with augmented reality (to convey the respective class information) we enable the robot to quickly acquire a significant amount of automatically labeled training data on its own. Training in a transfer learning fashion, we demonstrate the robot's capability to detect recently learned objects and evaluate the influence of different machine learning models and learning procedures as well as the amount of training data involved. Our multimodal approach proves to be an efficient and natural way to teach the robot novel objects based on a few instances and allows it to detect classes for which no training dataset is available. In addition, we make our dataset publicly available to the research community, which consists of RGB and depth data, intrinsic and extrinsic camera parameters, along with regions of interest.