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Found 28 papers, 6 papers with code
ToP-ToM: Trust-aware Robot Policy with Theory of Mind
In this paper, we investigated trust-aware robot policy with the theory of mind in a multiagent setting where a human collaborates with a robot against another human opponent.
LIPEx-Locally Interpretable Probabilistic Explanations-To Look Beyond The True Class
This data-efficiency is seen to manifest as LIPEx being able to compute its explanation matrix around 53% faster than all-class LIME, for classification experiments with text data.
To Whom are You Talking? A Deep Learning Model to Endow Social Robots with Addressee Estimation Skills
Communicating shapes our social word.
Proceeding of the 1st Workshop on Social Robots Personalisation At the crossroads between engineering and humanities (CONCATENATE)
However, achieving personalisation is arduous as it requires us to expand the boundaries of robotics by taking advantage of the expertise of various domains.
Towards Multi-User Activity Recognition through Facilitated Training Data and Deep Learning for Human-Robot Collaboration Applications
To validate this statement, 3D skeleton poses of activity of single users were collected and merged in pairs.
Signs of Language: Embodied Sign Language Fingerspelling Acquisition from Demonstrations for Human-Robot Interaction
Learning fine-grained movements is a challenging topic in robotics, particularly in the context of robotic hands.
CASPER: Cognitive Architecture for Social Perception and Engagement in Robots
We have tested this architecture in a simulated kitchen environment and the results we have collected show that the robot is able to both recognize an ongoing goal and to properly collaborate towards its achievement.
WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language
Signed Language Processing (SLP) concerns the automated processing of signed languages, the main means of communication of Deaf and hearing impaired individuals.
Human-robot collaboration and machine learning: a systematic review of recent research
Technological progress increasingly envisions the use of robots interacting with people in everyday life.
Phonology Recognition in American Sign Language
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.
The Challenges and Opportunities of Human-Centered AI for Trustworthy Robots and Autonomous Systems
Then, the challenges in implementing trustworthy autonomous system are analytically reviewed, in respects of the five key properties, and the roles of AI technologies have been explored to ensure the trustiness of RAS with respects to safety, security, health and HMI, while reflecting the requirements of ethics in the design of RAS.
When Would You Trust a Robot? A Study on Trust and Theory of Mind in Human-Robot Interactions
To this end, participants played a Price Game with a humanoid robot that was presented having either low level Theory of Mind or high level Theory of Mind.
Robotics
At Your Service: Coffee Beans Recommendation From a Robot Assistant
From this, we propose how this computational model can be deployed on a service robot to reliably predict customers' coffee bean preferences, starting from the user inputting their coffee preferences to the robot recommending the coffee beans that best meet the user's likings.
A robot that counts like a child: a developmental model of counting and pointing
In this paper, a novel neuro-robotics model capable of counting real items is introduced.
Towards a self-organizing pre-symbolic neural model representing sensorimotor primitives
During the learning process of observing sensorimotor primitives, i. e. observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units.
Human activity recognition from skeleton poses
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 environment.
Sample-efficient Deep Reinforcement Learning with Imaginary Rollouts for Human-Robot Interaction
Deep reinforcement learning has proven to be a great success in allowing agents to learn complex tasks.
Influence of Pointing on Learning to Count: A Neuro-Robotics Model
In this paper a neuro-robotics model capable of counting using gestures is introduced.
A Deep Neural Network for Finger Counting and Numerosity Estimation
The performance in number estimation of such an extended model is evaluated.
Encoding Longer-term Contextual Multi-modal Information in a Predictive Coding Model
On the other hand, the incoming sensory information corrects such prediction of the events on the higher level by the novel or surprising signal.
AFA-PredNet: The action modulation within predictive coding
The predictive processing (PP) hypothesizes that the predictive inference of our sensorimotor system is encoded implicitly in the regularities between perception and action.
Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers
Group emotion recognition in the wild is a challenging problem, due to the unstructured environments in which everyday life pictures are taken.
Autonomous Quadrotor Landing using Deep Reinforcement Learning
Landing an unmanned aerial vehicle (UAV) on a ground marker is an open problem despite the effort of the research community.
Where is my forearm? Clustering of body parts from simultaneous tactile and linguistic input using sequential mapping
In species that use language, this process is further structured by this interaction, where a mapping between the sensorimotor concepts and linguistic elements needs to be established.
Toward Abstraction from Multi-modal Data: Empirical Studies on Multiple Time-scale Recurrent Models
This was done by conducting two studies based on a smaller data- set (two-dimension time sequences from non-linear functions) and a relatively large data-set (43-dimension time sequences from iCub manipulation tasks with multi-modal data).
A Hierarchical Emotion Regulated Sensorimotor Model: Case Studies
Inspired by the hierarchical cognitive architecture and the perception-action model (PAM), we propose that the internal status acts as a kind of common-coding representation which affects, mediates and even regulates the sensorimotor behaviours.
Sensorimotor Input as a Language Generalisation Tool: A Neurorobotics Model for Generation and Generalisation of Noun-Verb Combinations with Sensorimotor Inputs
The paper presents a neurorobotics cognitive model to explain the understanding and generalisation of nouns and verbs combinations when a vocal command consisting of a verb-noun sentence is provided to a humanoid robot.
A cognitive neural architecture able to learn and communicate through natural language
Communicative interactions involve a kind of procedural knowledge that is used by the human brain for processing verbal and nonverbal inputs and for language production.
