Incremental Learning for Bootstrapping Object Classifier Models

International audienceMany state of the art object classification applications require many data samples, whose collection is usually a very costly process. Performing initial model training with synthetic samples (from virtual reality tools) has been proposed as a possible solution, although the resulting classification models need to be adapted (fine-tuned) to real-world data afterwards. In this paper, we propose to use an incremental learning from cognitive robotics, which is is particularly suited for perceptual problems, for this bootstrapping process. We apply it to a pedestrian detection problem where a synthetic dataset is used for initial training, and two different real-world datasets for fine-tuning and evaluation. The proposed scheme greatly reduces the number of real-world samples required while maintaining high classification accuracy. We also demonstrate an innovative incremental learning schemes for object detection which training object and background samples one after the other: this keeps models simple by representing only those background samples that can actually be confused with pedestrians

A Bio-Inspired Incremental Learning Architecture for Applied Perceptual Problems

International audienceWe present a biologically inspired architecture for incremental learning that remains resource-efficient even in the face of very high data dimensionalities (>1000) that are typically associated with perceptual problems. In particular, we investigate how a new perceptual (object) class can be added to a trained architecture without retraining, while avoiding the well-known catastrophic forgetting effects typically associated with such scenarios. At the heart of the presented architecture lies a generative description of the perceptual space by a self-organized approach which at the same time approximates the neighbourhood relations in this space on a two-dimensional plane. This approximation , which closely imitates the topographic organization of the visual cortex, allows an efficient local update rule for incremental learning even in the face of very high dimensionalities, which we demonstrate by tests on the well-known MNIST benchmark. We complement the model by adding a biologically plausible short-term memory system, allowing it to retain excellent classification accuracy even under incremental learning in progress. The short-term memory is additionally used to reinforce new data statistics by replaying previously stored samples during dedicated " sleep " phases

Learning of information gathering in modular intelligent systems

Karaoguz C. Learning of information gathering in modular intelligent systems. Bielefeld: Universitaetsbibliothek, Universitaet Bielefeld; 2012.Research of intelligent systems aims to realize autonomous agents capable of performing various functions to ease every day life of humans. Usually, such occupations can be formalized as a collection of tasks that have to be executed in parallel or in a sequence. Since real world environments are highly dynamic and unpredictable, intelligent systems require cognitive capabilities that can learn how to execute such tasks through interactions. Considering that the system has limited resources for acquiring and processing information, a strategy is required to find and update task-relevant information sources efficiently in time. This thesis proposes a system level approach for the information gathering process and an implementation that puts this idea into work. The presented framework takes a modular systems approach where modules are defined as elementary processing units for information acquisition and processing. The modular system design helps handling scenario complexity. A module management mechanism learns which modules deliver task relevant information and how the constrained system resources are distributed among these in a reward based framework. This reduces the partial observability caused by the information gathering process and provides better support to other high level cognitive functionalities of the system. Such an adaptive approach also makes it possible to deal with variations in the scenario or environment. Two different applications in simulation are implemented to test these hypotheses and demonstrate the utility of the proposed framework: the first implements a `reaching-while-interacting' scenario for a humanoid robot and the second employing an autonomous navigation scenario for a mobile robot. Both scenarios involve dynamic objects, rendering a challenging environment close to the real-world conditions for the system. Results from experiments with these applications provide evidence for hypotheses postulated in the thesis

Learning Information Acquisition for Multitasking Scenarios in Dynamic Environments

Karaoguz C, Rodemann T, Wrede B, Goerick C. Learning Information Acquisition for Multitasking Scenarios in Dynamic Environments. Ieee Transactions On Autonomous Mental Development. 2013;5(1):46-61.Real world environments are so dynamic and unpredictable that a goal-oriented autonomous system performing a set of tasks repeatedly never experiences the same situation even though the task routines are the same. Hence, manually designed solutions to execute such tasks are likely to fail due to such variations. Developmental approaches seek to solve this problem by implementing local learning mechanisms to the systems that can unfold capabilities to achieve a set of tasks through interactions with the environment. However, gathering all the information available in the environment for local learning mechanisms to process is hardly possible due to limited resources of the system. Thus, an information acquisition mechanism is necessary to find task-relevant information sources and applying a strategy to update the knowledge of the system about these sources efficiently in time. A modular systems approach may provide a useful structured and formalized basis for that. In such systems different modules may request access to the constrained system resources to acquire information they are tuned for. We propose a reward-based learning framework that achieves an efficient strategy for distributing the constrained system resources among modules to keep relevant environmental information up to date for higher level task learning and executing mechanisms in the system. We apply the proposed framework to a visual attention problem in a system using the iCub humanoid in simulation

A methodology for analyzing the impact of crosstalk on LIDAR measurements

International audienceLIDAR sensors are essential in intelligent transportation systems since they provide high-resolution, dense and precise range measurements. The use of LIDARs is rapidly growing and an increasing number of vehicles equipped with these sensors will share the road in a near future. An unfortunate consequence is that interference between LIDAR devices may occur. Indeed, crosstalk occurs when the laser beam emitted by a LIDAR disturbs the measurement process of another LIDAR. The analysis of the effect of crosstalk is therefore becoming crucial for assessing the performance of LIDAR devices and ensuring the safety of autonomous vehicles. This paper presents a detailed and reproducible methodology for evaluating the impact of crosstalk for LIDARs based on different technologies