Search Results for author:
Found 14 papers, 6 papers with code
Geometric Multimodal Contrastive Representation Learning
Learning representations of multimodal data that are both informative and robust to missing modalities at test time remains a challenging problem due to the inherent heterogeneity of data obtained from different channels.
Software architecture for YOLO, a creativity-stimulating robot
Additionally, this software allows the creation of Social Behaviors that enable the robot to behave as a believable character.
Playing Games in the Dark: An approach for cross-modality transfer in reinforcement learning
In this work we explore the use of latent representations obtained from multiple input sensory modalities (such as images or sounds) in allowing an agent to learn and exploit policies over different subsets of input modalities.
How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents
This work addresses the problem of sensing the world: how to learn a multimodal representation of a reinforcement learning agent's environment that allows the execution of tasks under incomplete perceptual conditions.
Learning multimodal representations for sample-efficient recognition of human actions
In this work we present \textit{motion concepts}, a novel multimodal representation of human actions in a household environment.
Explainable Agents Through Social Cues: A Review
One reason for this high variance in terminology is the unique array of social cues that embodied agents can access in contrast to that accessed by non-embodied agents.
MHVAE: a Human-Inspired Deep Hierarchical Generative Model for Multimodal Representation Learning
Humans are able to create rich representations of their external reality.
Explainable Agency by Revealing Suboptimality in Child-Robot Learning Scenarios
In the application scenario, the child and the robot learn together how to play a zero-sum game that requires logical and mathematical thinking.
FAtiMA Toolkit -- Toward an effective and accessible tool for the development of intelligent virtual agents and social robots
More than a decade has passed since the development of FearNot!, an application designed to help children deal with bullying through role-playing with virtual characters.
Decision Making
Multiagent Systems
Human-Computer Interaction
Robotics
Learning from Explanations and Demonstrations: A Pilot Study
AI has become prominent in a growing number of systems, and, as a direct consequence, the desire for explainability in such systems has become prominent as well.
Avant-Satie! Using ERIK to encode task-relevant expressivity into the animation of autonomous social robots
ERIK is an expressive inverse kinematics technique that has been previously presented and evaluated both algorithmically and in a limited user-interaction scenario.
"Guess what I'm doing": Extending legibility to sequential decision tasks
In this paper we investigate the notion of legibility in sequential decision tasks under uncertainty.
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning
We introduce hybrid execution in multi-agent reinforcement learning (MARL), a new paradigm in which agents aim to successfully complete cooperative tasks with arbitrary communication levels at execution time by taking advantage of information-sharing among the agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
From Modelling to Understanding Children's Behaviour in the Context of Robotics and Social Artificial Intelligence
Understanding and modelling children's cognitive processes and their behaviour in the context of their interaction with robots and social artificial intelligence systems is a fundamental prerequisite for meaningful and effective robot interventions.
