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Learning the rules establishing the DNA replication landscape – RepliLand
initiation. We propose to develop an artificial intelligence (AI) method based on neural network to infer
Learning for proximity flying – Proxilearn
The Proxilearn project leverages artificial intelligence techniques (machine learning) to allow micro-UAVs
Learning Reasoning, Memory and Behavior – REMEMBER
We will focus on methodological contributions (models and algorithms) for training virtual and real agents to learn to solve complex tasks autonomously, targeting terrestrial mobile robots, typically service robots; industrial cobotics; autonomous vehicles; UAVs; humanoid robots. In particular, intelligent agents require high-level reasoning capabilities, situation awareness, and the capacity of robustly taking the right decisions at the right moments. The required behavior policies are complex, since they involve high-dimensional input spaces and state spaces, partially observed problems, as well as highly non-linear and entangled interdependencies. Learning them crucially depends on the algorithm’s capacity of learning compact, structured and semantically meaningful memory representations, which are able to capture short and long range regularities in the task and the environment. A second key requirement is the ability to learn these representations with a minimal amount of human interventions and annotations, as the manual design of complex representations is up to impossible. This requires the efficient usage of raw data through the discovery of regularities by different means: supervised, unsupervised or self-supervised learning, through reward or intrinsic motivation etc.
Learning Intelligible Task Models for Cobots Programming – Prog4Yu
Learning of Intelligent Task Models for Cobot Programming
Learning Generalizable 3D-based Robotic Manipulation Policies – 3D-GEM
leveraging recent breakthroughs in artificial intelligence, particularly large language models and vision
Learning Cyclotron – LeCycl
is to augment and amplify human intelligence by artificial intelligence. In particular, we propose and
Learning And Reasoning for Deciding Optimally using Numerical and Symbolic information – LARDONS
The project aims at designing innovative, fondamental models and methods for enabling autonomous agents (e.g., mobile robots, personal software assistants) to learn and to act using all type of information. We target two specific types of information, namely: numerical information, like statistics or probabilities modelling effectors and sensors, and symbolic information, as typically provided by humans when they specify norms, obligations, constraints, etc.
Law Versioning: semantical and executable – LaVerSE
a language would improve the redaction and intelligibility of the law.
Large-scale coin die studies with artificial intelligence – STUDIES
In the domain of numismatics and ancient economy, there is a long expressed and large expectation to estimate the volumes of produced coinage. Study of coin-finds and hoards not being reliable, die st
Large-scale Optimization of Product Flows – LOPF
uses the latest breakthroughs in artificial intelligence (AI) and big data.3 Whether it is logistics [...] about predictive decision support systems and intelligent customer service. The Laboratory of Probability
