Sébastien Jodogne

Learn More
In this paper we present a general, flexible framework for learning mappings from images to actions by interacting with the environment. The basic idea is to introduce a feature-based image classifier in front of a reinforcement learning algorithm. The classifier partitions the visual space according to the presence or absence of few highly informative(More)
This article considers finite-automata-based algorithms for handling linear arithmetic with both real and integer variables. Previous work has shown that this theory can be dealt with by using finite automata on infinite words, but this involves some difficult and delicate to implement algorithms. The contribution of this article is to show, using(More)
This paper addresses the problem of computing an exact and effective representation of the set of reachable configurations of a linear hybrid automaton. Our solution is based on accelerating the state-space exploration by computing symbolically the repeated effect of control cycles. The computed sets of configurations are represented by Real Vector Automata(More)
This paper considers finite-automata based algorithms for handling linear arithmetic with both real and integer variables. Previous work has shown that this theory can be dealt with by using finite au-tomata on infinite words, but this involves some difficult and delicate to implement algorithms. The contribution of this paper is to show, using topological(More)
We discuss vision as a sensory modality for systems that effect actions in response to perceptions. While the internal representations informed by vision may be arbitrarily complex, we argue that in many cases it is advantageous to link them rather directly to action via learned mappings. These arguments are illustrated by two examples of our own work.(More)
We address the topic of real-time analysis and recognition of silhouettes. The method that we propose first produces object features obtained by a new type of morphological operators, which can be seen as an extension of existing granulometric filters, and then insert them into a tailored classification scheme. Intuitively, given a binary segmented image,(More)
We introduce flexible algorithms that can automatically learn mappings from images to actions by interacting with their environment. They work by introducing an image classifier in front of a Reinforcement Learning algorithm. The classifier partitions the visual space according to the presence or absence of highly informative local descriptors. The image(More)
Scheduling Data-Intensive Bags of Tasks in P2P Grids leads to transfers of large input data files, which cause delays in completion times. We propose to combine several existing technologies and patterns to perform efficient data-aware scheduling: (1) use of the BitTorrent P2P file sharing protocol to transfer data, (2) data caching on computational(More)
Approximate Policy Iteration (API) is a reinforcement learning paradigm that is able to solve high-dimensional, continuous control problems. We propose to exploit API for the closed-loop learning of map-pings from images to actions. This approach requires a family of function approximators that maps visual percepts to a real-valued function. For this(More)
Solving a visual, interactive task can often be thought of as building a mapping from visual stimuli to appropriate actions. Clearly, the extracted visual characteristics that index into the repertoire of actions must be sufficiently rich to distinguish situations that demand distinct actions. Spatial combinations of local features permit, in principle, the(More)