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.
In this work, we instantiate a novel perturbation-based multi-class explanation framework, LIPEx (Locally Interpretable Probabilistic Explanation).
no code implementations • 10 Jul 2023 • Imene Tarakli, Georgios Angelopoulos, Mehdi Hellou, Camille Vindolet, Boris Abramovic, Rocco Limongelli, Dimitri Lacroix, Andrea Bertolini, Silvia Rossi, Alessandro Di Nuovo, Angelo Cangelosi, Gordon Cheng
However, achieving personalisation is arduous as it requires us to expand the boundaries of robotics by taking advantage of the expertise of various domains.
To validate this statement, 3D skeleton poses of activity of single users were collected and merged in pairs.
Learning fine-grained movements is a challenging topic in robotics, particularly in the context of robotic hands.
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.
Signed Language Processing (SLP) concerns the automated processing of signed languages, the main means of communication of Deaf and hearing impaired individuals.
Technological progress increasingly envisions the use of robots interacting with people in everyday life.
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.
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.
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.
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.
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 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.
Deep reinforcement learning has proven to be a great success in allowing agents to learn complex tasks.
On the other hand, the incoming sensory information corrects such prediction of the events on the higher level by the novel or surprising signal.
The predictive processing (PP) hypothesizes that the predictive inference of our sensorimotor system is encoded implicitly in the regularities between perception and action.
Group emotion recognition in the wild is a challenging problem, due to the unstructured environments in which everyday life pictures are taken.
Landing an unmanned aerial vehicle (UAV) on a ground marker is an open problem despite the effort of the research community.
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.
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).
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.
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.
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.