118 citations · 200 across the 7 of their papers we have counts for
23 papers
Are standard Object Segmentation models sufficient for Learning Affordance Segmentation?
Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
Affordances are the possibilities of actions the environment offers to the individual. Ordinary objects (hammer, knife) usually have many affordances (grasping, pounding, cutting),…
SCOD: Active Object Detection for Embodied Agents using Sensory Commutativity of Action Sequences
Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
We introduce SCOD (Sensory Commutativity Object Detection), an active method for movable and immovable object detection. SCOD exploits the commutative properties of action sequence…
Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey
Thomas Rojat, Raphaël Puget, David Filliat +3
Most of state of the art methods applied on time series consist of deep learning methods that are too complex to be interpreted. This lack of interpretability is a major drawback,…
DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics
Stephane Doncieux, Nicolas Bredeche, Léni Le Goff +9
Robots are still limited to controlled conditions, that the robot designer knows with enough details to endow the robot with the appropriate models or behaviors. Learning algorithm…
On the Sensory Commutativity of Action Sequences for Embodied Agents
Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
Perception of artificial agents is one the grand challenges of AI research. Deep Learning and data-driven approaches are successful on constrained problems where perception can be…
Regularization Shortcomings for Continual Learning
Timothée Lesort, Andrei Stoian, David Filliat
In most machine learning algorithms, training data is assumed to be independent and identically distributed (iid). When it is not the case, the algorithm's performances are challen…