In this paper we introduce MCA-NMF, a computational model of the acquisition of multimodal concep... more In this paper we introduce MCA-NMF, a computational model of the acquisition of multimodal concepts by an agent grounded in its environment. More precisely our model finds patterns in multimodal sensor input that characterize associations across modalities. We propose this computational model as an answer to the question of how some class of concepts can be learnt. The model is also a way of defining such a class of plausibly learnable concepts. We detail why the multimodal nature of perception is essential to lower the ambiguity of learnt concepts as well as communicate about them. We then present a set of experiments that demonstrate the learning of such concepts from real non-symbolic data consisting of speech sounds, images, and motion acquisitions. Finally we consider structure in perceptual signals and demonstrate that a detailed knowledge of this structure, named compositional understanding can emerge from, instead of being a prerequisite of, global understanding. An open-sou...
ABSTRACT The recent availability of inexpensive RGB-D cameras, such as the Microsoft Kinect, has ... more ABSTRACT The recent availability of inexpensive RGB-D cameras, such as the Microsoft Kinect, has raised interest in the robotics community for point cloud segmentation. We are interested in the semantic segmentation task in which the goal is to find some relevant classes for navigation, wall, ground, objects, etc. Several effective solutions have been proposed, mainly based on the recursive decomposition of the point cloud into planes. We compare such a solution to a non-associative MRF method inspired by some recent work in computer vision. The MRF yields interesting results that are however less good than those of a carefully tuned geometric method. Nevertheless, MRF still has some advantages and we suggest some improvements.
In this paper we introduce MCA-NMF, a computational model of the acquisition of multimodal concep... more In this paper we introduce MCA-NMF, a computational model of the acquisition of multimodal concepts by an agent grounded in its environment. More precisely our model finds patterns in multimodal sensor input that characterize associations across modalities. We propose this computational model as an answer to the question of how some class of concepts can be learnt. The model is also a way of defining such a class of plausibly learnable concepts. We detail why the multimodal nature of perception is essential to lower the ambiguity of learnt concepts as well as communicate about them. We then present a set of experiments that demonstrate the learning of such concepts from real non-symbolic data consisting of speech sounds, images, and motion acquisitions. Finally we consider structure in perceptual signals and demonstrate that a detailed knowledge of this structure, named compositional understanding can emerge from, instead of being a prerequisite of, global understanding. An open-sou...
ABSTRACT The recent availability of inexpensive RGB-D cameras, such as the Microsoft Kinect, has ... more ABSTRACT The recent availability of inexpensive RGB-D cameras, such as the Microsoft Kinect, has raised interest in the robotics community for point cloud segmentation. We are interested in the semantic segmentation task in which the goal is to find some relevant classes for navigation, wall, ground, objects, etc. Several effective solutions have been proposed, mainly based on the recursive decomposition of the point cloud into planes. We compare such a solution to a non-associative MRF method inspired by some recent work in computer vision. The MRF yields interesting results that are however less good than those of a carefully tuned geometric method. Nevertheless, MRF still has some advantages and we suggest some improvements.
Uploads
Papers by David Filliat