41 citations · 189 across the 29 of their papers we have counts for
30 papers
Humanoid Horizon: Extending Task Horizon in Whole-Body Loco-Manipulation via Parallel Training, Dynamic Starting, and Reward Gating
Haozhuo Zhang, Qiang Zhang, Jian Tang +4
Cluttered indoor environments, where large and heavy objects are scattered across diverse surfaces, require humanoid robots to sequentially navigate, grasp, transport, and accurate…
LUCID: Latent-Skill Unified Control via Imagined Dynamics for Long-Horizon Humanoid Loco-Manipulation
Cheng Guo, Mingzhe Ni, Angelo Cangelosi +1
Long-horizon humanoid loco-manipulation requires composing versatile whole-body skills and reliable high-level decision making. Existing methods often coordinate pretrained skills…
Pay Attention to What and Where? Interpretable Feature Extractor in Vision-based Deep Reinforcement Learning
Tien Pham, Angelo Cangelosi
Current approaches in Explainable Deep Reinforcement Learning have limitations in which the attention mask has a displacement with the objects in visual input. This work addresses…
To Whom are You Talking? A Deep Learning Model to Endow Social Robots with Addressee Estimation Skills
Carlo Mazzola, Marta Romeo, Francesco Rea +2
Communicating shapes our social word. For a robot to be considered social and being consequently integrated in our social environment it is fundamental to understand some of the dy…
Proceeding of the 1st Workshop on Social Robots Personalisation At the crossroads between engineering and humanities (CONCATENATE)
Imene Tarakli, Georgios Angelopoulos, Mehdi Hellou +9
Nowadays, robots are expected to interact more physically, cognitively, and socially with people. They should adapt to unpredictable contexts alongside individuals with various beh…
Towards Multi-User Activity Recognition through Facilitated Training Data and Deep Learning for Human-Robot Collaboration Applications
Francesco Semeraro, Jon Carberry, Angelo Cangelosi
Human-robot interaction (HRI) research is progressively addressing multi-party scenarios, where a robot interacts with more than one human user at the same time. Conversely, resear…
Signs of Language: Embodied Sign Language Fingerspelling Acquisition from Demonstrations for Human-Robot Interaction
Federico Tavella, Aphrodite Galata, Angelo Cangelosi
Learning fine-grained movements is a challenging topic in robotics, particularly in the context of robotic hands. One specific instance of this challenge is the acquisition of fing…
CASPER: Cognitive Architecture for Social Perception and Engagement in Robots
Samuele Vinanzi, Angelo Cangelosi
Our world is being increasingly pervaded by intelligent robots with varying degrees of autonomy. To seamlessly integrate themselves in our society, these machines should possess th…
WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language
Federico Tavella, Viktor Schlegel, Marta Romeo +2
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…
Human-robot collaboration and machine learning: a systematic review of recent research
Francesco Semeraro, Alexander Griffiths, Angelo Cangelosi
Technological progress increasingly envisions the use of robots interacting with people in everyday life. Human-robot collaboration (HRC) is the approach that explores the interact…
Phonology Recognition in American Sign Language
Federico Tavella, Aphrodite Galata, Angelo Cangelosi
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…
The Challenges and Opportunities of Human-Centered AI for Trustworthy Robots and Autonomous Systems
Hongmei He, John Gray, Angelo Cangelosi +3
The trustworthiness of Robots and Autonomous Systems (RAS) has gained a prominent position on many research agendas towards fully autonomous systems. This research systematically e…
When Would You Trust a Robot? A Study on Trust and Theory of Mind in Human-Robot Interactions
Wenxuan Mou, Martina Ruocco, Debora Zanatto +1
Trust is a critical issue in Human Robot Interactions as it is the core of human desire to accept and use a non human agent. Theory of Mind has been defined as the ability to under…
At Your Service: Coffee Beans Recommendation From a Robot Assistant
Jacopo de Berardinis, Gabriella Pizzuto, Francesco Lanza +3
With advances in the field of machine learning, precisely algorithms for recommendation systems, robot assistants are envisioned to become more present in the hospitality industry.…
A robot that counts like a child: a developmental model of counting and pointing
Leszek Pecyna, Angelo Cangelosi, Alessandro Di Nuovo
In this paper, a novel neuro-robotics model capable of counting real items is introduced. The model allows us to investigate the interaction between embodiment and numerical cognit…
Towards a self-organizing pre-symbolic neural model representing sensorimotor primitives
Junpei Zhong, Angelo Cangelosi, Stefan Wermter
The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and other…
Human activity recognition from skeleton poses
Frederico Belmonte Klein, Angelo Cangelosi
Human Action Recognition is an important task of Human Robot Interaction as cooperation between robots and humans requires that artificial agents recognise complex cues from the en…
Sample-efficient Deep Reinforcement Learning with Imaginary Rollouts for Human-Robot Interaction
Mohammad Thabet, Massimiliano Patacchiola, Angelo Cangelosi
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. Fu…
Influence of Pointing on Learning to Count: A Neuro-Robotics Model
Leszek Pecyna, Angelo Cangelosi
In this paper a neuro-robotics model capable of counting using gestures is introduced. The contribution of gestures to learning to count is tested with various model and training c…
A Deep Neural Network for Finger Counting and Numerosity Estimation
Leszek Pecyna, Angelo Cangelosi, Alessandro Di Nuovo
In this paper, we present neuro-robotics models with a deep artificial neural network capable of generating finger counting positions and number estimation. We first train the mode…
Encoding Longer-term Contextual Multi-modal Information in a Predictive Coding Model
Junpei Zhong, Tetsuya Ogata, Angelo Cangelosi
Studies suggest that within the hierarchical architecture, the topological higher level possibly represents a conscious category of the current sensory events with slower changing…
AFA-PredNet: The action modulation within predictive coding
Junpei Zhong, Angelo Cangelosi, Xinzheng Zhang +1
The predictive processing (PP) hypothesizes that the predictive inference of our sensorimotor system is encoded implicitly in the regularities between perception and action. We pro…
Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers
Luca Surace, Massimiliano Patacchiola, Elena Battini Sönmez +2
Group emotion recognition in the wild is a challenging problem, due to the unstructured environments in which everyday life pictures are taken. Some of the obstacles for an effecti…
Autonomous Quadrotor Landing using Deep Reinforcement Learning
Riccardo Polvara, Massimiliano Patacchiola, Sanjay Sharma +4
Landing an unmanned aerial vehicle (UAV) on a ground marker is an open problem despite the effort of the research community. Previous attempts mostly focused on the analysis of han…