Search Results for author:
Found 18 papers, 3 papers with code
Reconciling Spatial and Temporal Abstractions for Goal Representation
In this paper, we propose a novel three-layer HRL algorithm that introduces, at different levels of the hierarchy, both a spatial and a temporal goal abstraction.
Unsupervised Motion Retargeting for Human-Robot Imitation
This early-stage research work aims to improve online human-robot imitation by translating sequences of joint positions from the domain of human motions to a domain of motions achievable by a given robot, thus constrained by its embodiment.
Prerequisite Structure Discovery in Intelligent Tutoring Systems
This paper addresses the importance of Knowledge Structure (KS) and Knowledge Tracing (KT) in improving the recommendation of educational content in intelligent tutoring systems.
Goal Space Abstraction in Hierarchical Reinforcement Learning via Set-Based Reachability Analysis
Open-ended learning benefits immensely from the use of symbolic methods for goal representation as they offer ways to structure knowledge for efficient and transferable learning.
Goal Space Abstraction in Hierarchical Reinforcement Learning via Reachability Analysis
Open-ended learning benefits immensely from the use of symbolic methods for goal representation as they offer ways to structure knowledge for efficient and transferable learning.
Robots Learn Increasingly Complex Tasks with Intrinsic Motivation and Automatic Curriculum Learning
Multi-task learning by robots poses the challenge of the domain knowledge: complexity of tasks, complexity of the actions required, relationship between tasks for transfer learning.
Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart Homes
We thus demonstrate that taking into account the context of the sensors and their semantics increases the classification performances and enables transfer learning.
A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning
In this paper, we present recent algorithms, works, challenges and taxonomy of the field of human activity recognition in a smart home through ambient sensors.
Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task Hierarchy
We hypothesise that the most complex tasks can be learned more easily by transferring knowledge from simpler tasks, and faster by adapting the complexity of the actions to the task.
Hierarchical Affordance Discovery using Intrinsic Motivation
To be capable of lifelong learning in a real-life environment, robots have to tackle multiple challenges.
How An Automated Gesture Imitation Game Can Improve Social Interactions With Teenagers With ASD
With the outlook of improving communication and social abilities of people with ASD, we propose to extend the paradigm of robot-based imitation games to ASD teenagers.
Building an Automated Gesture Imitation Game for Teenagers with ASD
Autism spectrum disorder is a neurodevelopmental condition that includes issues with communication and social interactions.
An implementation of an imitation game with ASD children to learn nursery rhymes
Previous studies have suggested that being imitated by an adult is an effective intervention with children with autism and developmental delay.
Generating Shared Latent Variables for Robots to Imitate Human Movements and Understand their Physical Limitations
Assistive robotics and particularly robot coaches may be very helpful for rehabilitation healthcare.
Learning a Set of Interrelated Tasks by Using Sequences of Motor Policies for a Strategic Intrinsically Motivated Learner
We propose an active learning architecture for robots, capable of organizing its learning process to achieve a field of complex tasks by learning sequences of motor policies, called Intrinsically Motivated Procedure Babbling (IM-PB).
Socially Guided Intrinsic Motivation for Robot Learning of Motor Skills
This paper presents a technical approach to robot learning of motor skills which combines active intrinsically motivated learning with imitation learning.
Active choice of teachers, learning strategies and goals for a socially guided intrinsic motivation learner
It has to choose actively and hierarchically at each learning episode: 1) what to learn: which outcome is most interesting to select as a goal to focus on for goal-directed exploration; 2) how to learn: which data collection strategy to use among self-exploration, mimicry and emulation; 3) once he has decided when and what to imitate by choosing mimicry or emulation, then he has to choose who to imitate, from a set of different teachers.
Real-time face swapping as a tool for understanding infant self-recognition
To study the preference of infants for contingency of movements and familiarity of faces during self-recognition task, we built, as an accurate and instantaneous imitator, a real-time face- swapper for videos.
