no code implementations • 8 Apr 2024 • Hugo Caselles-Dupré, Charles Mellerio, Paul Hérent, Alizée Lopez-Persem, Benoit Béranger, Mathieu Soularue, Pierre Fautrel, Gauthier Vernier, Matthieu Cord
The reconstruction of images observed by subjects from fMRI data collected during visual stimuli has made strong progress in the past decade, thanks to the availability of extensive fMRI datasets and advancements in generative models for image generation.
1 code implementation • 29 Sep 2023 • Clémence Grislain, Hugo Caselles-Dupré, Olivier Sigaud, Mohamed Chetouani
To this end, human teachers seem to build mental models of the learner's internal state, a capacity known as Theory of Mind (ToM).
no code implementations • 18 Aug 2023 • Hugo Caselles-Dupré, Olivier Sigaud, Mohamed Chetouani
We introduce a novel category of GC-agents capable of functioning as both teachers and learners.
no code implementations • 26 Sep 2022 • Hugo Caselles-Dupré, Olivier Sigaud, Mohamed Chetouani
Teaching an agent to perform new tasks using natural language can easily be hindered by ambiguities in interpretation.
1 code implementation • 9 Jun 2022 • Hugo Caselles-Dupré, Olivier Sigaud, Mohamed Chetouani
In this paper, we implement pedagogy and pragmatism mechanisms by leveraging a Bayesian model of Goal Inference from demonstrations (BGI).
no code implementations • 28 Feb 2022 • Hugo Caselles-Dupré, Mohamed Chetouani, Olivier Sigaud
When demonstrating a task, human tutors pedagogically modify their behavior by either "showing" the task rather than just "doing" it (exaggerating on relevant parts of the demonstration) or by giving demonstrations that best disambiguate the communicated goal.
no code implementations • 5 Jul 2021 • Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
We conclude that the problem of supervised affordance segmentation is included in the problem of object segmentation and argue that better benchmarks for affordance learning should include action capacities.
no code implementations • 5 Jul 2021 • Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
We introduce SCOD (Sensory Commutativity Object Detection), an active method for movable and immovable object detection.
no code implementations • 25 May 2021 • Olivier Sigaud, Ahmed Akakzia, Hugo Caselles-Dupré, Cédric Colas, Pierre-Yves Oudeyer, Mohamed Chetouani
In the field of Artificial Intelligence, these extremes respectively map to autonomous agents learning from their own signals and interactive learning agents fully taught by their teachers.
no code implementations • 13 Feb 2020 • Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
In such case, for autonomous embodied agents with first-person sensors, perception can be learned end-to-end to solve particular tasks.
no code implementations • 11 Jul 2019 • René Traoré, Hugo Caselles-Dupré, Timothée Lesort, Te Sun, Guanghang Cai, Natalia Díaz-Rodríguez, David Filliat
In multi-task reinforcement learning there are two main challenges: at training time, the ability to learn different policies with a single model; at test time, inferring which of those policies applying without an external signal.
no code implementations • 11 Jun 2019 • René Traoré, Hugo Caselles-Dupré, Timothée Lesort, Te Sun, Natalia Díaz-Rodríguez, David Filliat
We focus on the problem of teaching a robot to solve tasks presented sequentially, i. e., in a continual learning scenario.
1 code implementation • NeurIPS 2019 • Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
Finding a generally accepted formal definition of a disentangled representation in the context of an agent behaving in an environment is an important challenge towards the construction of data-efficient autonomous agents.
no code implementations • 25 Feb 2019 • Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
As the environment changes, the aim is to efficiently compress the sensory state's information without losing past knowledge, and then use Reinforcement Learning on the resulting features for efficient policy learning.
1 code implementation • ICLR 2019 • Timothée Lesort, Hugo Caselles-Dupré, Michael Garcia-Ortiz, Andrei Stoian, David Filliat
We experiment with sequential tasks on three commonly used benchmarks for Continual Learning (MNIST, Fashion MNIST and CIFAR10).
no code implementations • 9 Oct 2018 • Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
As the environment changes, the aim is to efficiently compress the sensory state's information without losing past knowledge.
no code implementations • 3 Sep 2018 • Hugo Caselles-Dupré, Louis Annabi, Oksana Hagen, Michael Garcia-Ortiz, David Filliat
Flatland is a simple, lightweight environment for fast prototyping and testing of reinforcement learning agents.
1 code implementation • 11 Apr 2018 • Hugo Caselles-Dupré, Florian Lesaint, Jimena Royo-Letelier
Skip-gram with negative sampling, a popular variant of Word2vec originally designed and tuned to create word embeddings for Natural Language Processing, has been used to create item embeddings with successful applications in recommendation.