Collaborating Authors

Let me join you! Real-time F-formation recognition by a socially aware robot Artificial Intelligence

This paper presents a novel architecture to detect social groups in real-time from a continuous image stream of an ego-vision camera. F-formation defines social orientations in space where two or more person tends to communicate in a social place. Thus, essentially, we detect F-formations in social gatherings such as meetings, discussions, etc. and predict the robot's approach angle if it wants to join the social group. Additionally, we also detect outliers, i.e., the persons who are not part of the group under consideration. Our proposed pipeline consists of -- a) a skeletal key points estimator (a total of 17) for the detected human in the scene, b) a learning model (using a feature vector based on the skeletal points) using CRF to detect groups of people and outlier person in a scene, and c) a separate learning model using a multi-class Support Vector Machine (SVM) to predict the exact F-formation of the group of people in the current scene and the angle of approach for the viewing robot. The system is evaluated using two data-sets. The results show that the group and outlier detection in a scene using our method establishes an accuracy of 91%. We have made rigorous comparisons of our systems with a state-of-the-art F-formation detection system and found that it outperforms the state-of-the-art by 29% for formation detection and 55% for combined detection of the formation and approach angle.

Towards Social Artificial Intelligence: Nonverbal Social Signal Prediction in A Triadic Interaction Artificial Intelligence

We present a new research task and a dataset to understand human social interactions via computational methods, to ultimately endow machines with the ability to encode and decode a broad channel of social signals humans use. This research direction is essential to make a machine that genuinely communicates with humans, which we call Social Artificial Intelligence. We first formulate the "social signal prediction" problem as a way to model the dynamics of social signals exchanged among interacting individuals in a data-driven way. We then present a new 3D motion capture dataset to explore this problem, where the broad spectrum of social signals (3D body, face, and hand motions) are captured in a triadic social interaction scenario. Baseline approaches to predict speaking status, social formation, and body gestures of interacting individuals are presented in the defined social prediction framework.

Modeling Social Group Communication with Multi-Agent Imitation Learning Artificial Intelligence

In crowded social scenarios with a myriad of external stimuli, human brains exhibit a natural ability to filter out irrelevant information and narrowly focus their attention. In the midst of multiple groups of people, humans use such sensory gating to effectively further their own group's interactional goals. In this work, we consider the design of a policy network to model multi-group multi-person communication. Our policy takes as input the state of the world such as an agent's gaze direction, body pose of other agents or history of past actions, and outputs an optimal action such as speaking, listening or responding (communication modes). Inspired by humans' natural neurobiological filtering process, a central component of our policy network design is an information gating function, termed the Kinesic-Proxemic-Message Gate (KPM-Gate), that models the ability of an agent to selectively gather information from specific neighboring agents. The degree of influence of a neighbor is based on dynamic non-verbal cues such as body motion, head pose (kinesics) and interpersonal space (proxemics). We further show that the KPM-Gate can be used to discover social groups using its natural interpretation as a social attention mechanism. We pose the communication policy learning problem as a multi-agent imitation learning problem. We learn a single policy shared by all agents under the assumption of a decentralized Markov decision process. We term our policy network as the Multi-Agent Group Discovery and Communication Mode Network (MAGDAM network), as it learns social group structure in addition to the dynamics of group communication. Our experimental validation on both synthetic and real world data shows that our model is able to both discover social group structure and learn an accurate multi-agent communication policy.

I can attend a meeting too! Towards a human-like telepresence avatar robot to attend meeting on your behalf Artificial Intelligence

Telepresence robots are used in various forms in various use-cases that helps to avoid physical human presence at the scene of action. In this work, we focus on a telepresence robot that can be used to attend a meeting remotely with a group of people. Unlike a one-to-one meeting, participants in a group meeting can be located at a different part of the room, especially in an informal setup. As a result, all of them may not be at the viewing angle of the robot, a.k.a. the remote participant. In such a case, to provide a better meeting experience, the robot should localize the speaker and bring the speaker at the center of the viewing angle. Though sound source localization can easily be done using a microphone-array, bringing the speaker or set of speakers at the viewing angle is not a trivial task. First of all, the robot should react only to a human voice, but not to the random noises. Secondly, if there are multiple speakers, to whom the robot should face or should it rotate continuously with every new speaker? Lastly, most robotic platforms are resource-constrained and to achieve a real-time response, i.e., avoiding network delay, all the algorithms should be implemented within the robot itself. This article presents a study and implementation of an attention shifting scheme in a telepresence meeting scenario which best suits the needs and expectations of the collocated and remote attendees. We define a policy to decide when a robot should rotate and how much based on real-time speaker localization. Using user satisfaction study, we show the efficacy and usability of our system in the meeting scenario. Moreover, our system can be easily adapted to other scenarios where multiple people are located.

Finding Dory in the Crowd: Detecting Social Interactions using Multi-Modal Mobile Sensing Machine Learning

Remembering our day-to-day social interactions is challenging even if you aren't a blue memory challenged fish. The ability to automatically detect and remember these types of interactions is not only beneficial for individuals interested in their behavior in crowded situations, but also of interest to those who analyze crowd behavior. Currently, detecting social interactions is often performed using a variety of methods including ethnographic studies, computer vision techniques and manual annotation-based data analysis. However, mobile phones offer easier means for data collection that is easy to analyze and can preserve the user's privacy. In this work, we present a system for detecting stationary social interactions inside crowds, leveraging multi-modal mobile sensing data such as Bluetooth Smart (BLE), accelerometer and gyroscope. To inform the development of such system, we conducted a study with 24 participants, where we asked them to socialize with each other for 45 minutes. We built a machine learning system based on gradient-boosted trees that predicts both 1:1 and group interactions with 77.8% precision and 86.5% recall, a 30.2% performance increase compared to a proximity-based approach. By utilizing a community detection based method, we further detected the various group formation that exist within the crowd. Using mobile phone sensors already carried by the majority of people in a crowd makes our approach particularly well suited to real-life analysis of crowd behaviour and influence strategies.