11 accepted papers
Nonverbal communication plays a crucial role in both human-human and human-robot interactions (HRIs), where facial expressions convey emotions, intentions and trust. Enabling humanoid robots to generate human-like facial reactions in response to human speech and facial behaviours remains significant
Multimodal Emotion Recognition in Conversation (MERC) is an important element in human-machine interaction. It allows machines to automatically identify and track the emotional status of speakers during a conversation in a multimodal setting. However, the conversations involving various audio and vi…
Human-robot collaboration has great potential in enhancing robot deployment at close proximity with people, especially in non-dyadic collaborations with multiple users. However, autonomous systems that are capable of handling such interactions in a physical domain are rare. This work proposes TriHRC
Human-robot collaboration has the potential of unlocking new manufacturing paradigms thanks to the introduction of a robotic architecture in a production chain that involves human workers. A possible innovative declination of this is the use of collaborative robots to enable two workers to concurren…
Theory of Mind (ToM) is a fundamental cognitive architecture that endows humans with the ability to attribute mental states to others. Humans infer the desires, beliefs, and intentions of others by observing their behavior and, in turn, adjust their actions to facilitate better interpersonal communi…
Inspired by recent developments in natural language processing, we propose a novel approach to sign language processing based on phonological properties validated by American Sign Language users. By taking advantage of datasets composed of phonological data and people speaking sign language, we use…
Signed Language Processing (SLP) concerns the automated processing of signed languages, the main means of communication of Deaf and hearing impaired individuals. SLP features many different tasks, ranging from sign recognition to translation and production of signed speech, but has been overlooked b…
In this paper we present a neural architecture to learn a bi-directional mapping between actions and language. We implement a Multiple Timescale Long Short-Term Memory (MT-LSTM) network comprised of 7 layers with different timescale factors, to connect actions to language without explicitly learning…
The employment of Social Assistive Robots (SARs) for monitoring elderly users represents a valuable gateway for at-home assistance. Their deployment in the house of the users can provide effective opportunities for early detection of Mild Cognitive Impairment (MCI), a condition of increasing impact…
Deep reinforcement learning has proven to be a great success in allowing agents to learn complex tasks. However, its application to actual robots can be prohibitively expensive. Furthermore, the unpredictability of human behavior in human-robot interaction tasks can hinder convergence to a good poli…
How can high dimensional robots learn general sets of skills from experience in the real world? Many previous approaches focus on maximizing a single utility function and require large datasets of experience to do this, something that is not possible to collect outside of simulation as every data po…