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 • 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.
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.