Search Results for author:
Found 9 papers, 4 papers with code
Contrastive Multimodal Learning for Emergence of Graphical Sensory-Motor Communication
In this paper, we investigate whether artificial agents can develop a shared language in an ecological setting where communication relies on a sensory-motor channel.
Language and Culture Internalisation for Human-Like Autotelic AI
Building autonomous agents able to grow open-ended repertoires of skills across their lives is a fundamental goal of artificial intelligence (AI).
Learning to Guide and to Be Guided in the Architect-Builder Problem
ABIG results in a low-level, high-frequency, guiding communication protocol that not only enables an architect-builder pair to solve the task at hand, but that can also generalize to unseen tasks.
Grounding Spatio-Temporal Language with Transformers
While there is an extended literature studying how machines can learn grounded language, the topic of how to learn spatio-temporal linguistic concepts is still largely uncharted.
Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Survey
Developmental RL is concerned with the use of deep RL algorithms to tackle a developmental problem -- the $intrinsically$ $motivated$ $acquisition$ $of$ $open$-$ended$ $repertoires$ $of$ $skills$.
Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration
We argue that the ability to imagine out-of-distribution goals is key to enable creative discoveries and open-ended learning.
Language-Goal Imagination to Foster Creative Exploration in Deep RL
We introduce the Playground environment and study how this form of goal imagination improves generalization and exploration over agents lacking this capacity.
Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Exploration
We argue that the ability to imagine out-of-distribution goals is key to enable creative discoveries and open-ended learning.
