559 research outputs found
Predictive Coding Strategies for Developmental Neurorobotics
In recent years, predictive coding strategies have been proposed as a possible means by which the brain might make sense of the truly overwhelming amount of sensory data available to the brain at any given moment of time. Instead of the raw data, the brain is hypothesized to guide its actions by assigning causal beliefs to the observed error between what it expects to happen and what actually happens. In this paper, we present a variety of developmental neurorobotics experiments in which minimalist prediction error-based encoding strategies are utilize to elucidate the emergence of infant-like behavior in humanoid robotic platforms. Our approaches will be first naively Piagian, then move onto more Vygotskian ideas. More specifically, we will investigate how simple forms of infant learning, such as motor sequence generation, object permanence, and imitation learning may arise if minimizing prediction errors are used as objective functions
Explain what you see:argumentation-based learning and robotic vision
In this thesis, we have introduced new techniques for the problems of open-ended learning, online incremental learning, and explainable learning. These methods have applications in the classification of tabular data, 3D object category recognition, and 3D object parts segmentation. We have utilized argumentation theory and probability theory to develop these methods. The first proposed open-ended online incremental learning approach is Argumentation-Based online incremental Learning (ABL). ABL works with tabular data and can learn with a small number of learning instances using an abstract argumentation framework and bipolar argumentation framework. It has a higher learning speed than state-of-the-art online incremental techniques. However, it has high computational complexity. We have addressed this problem by introducing Accelerated Argumentation-Based Learning (AABL). AABL uses only an abstract argumentation framework and uses two strategies to accelerate the learning process and reduce the complexity. The second proposed open-ended online incremental learning approach is the Local Hierarchical Dirichlet Process (Local-HDP). Local-HDP aims at addressing two problems of open-ended category recognition of 3D objects and segmenting 3D object parts. We have utilized Local-HDP for the task of object part segmentation in combination with AABL to achieve an interpretable model to explain why a certain 3D object belongs to a certain category. The explanations of this model tell a user that a certain object has specific object parts that look like a set of the typical parts of certain categories. Moreover, integrating AABL and Local-HDP leads to a model that can handle a high degree of occlusion
MonoSLAM: Real-time single camera SLAM
Published versio
RL-LABEL: A Deep Reinforcement Learning Approach Intended for AR Label Placement in Dynamic Scenarios
Labels are widely used in augmented reality (AR) to display digital
information. Ensuring the readability of AR labels requires placing them
occlusion-free while keeping visual linkings legible, especially when multiple
labels exist in the scene. Although existing optimization-based methods, such
as force-based methods, are effective in managing AR labels in static
scenarios, they often struggle in dynamic scenarios with constantly moving
objects. This is due to their focus on generating layouts optimal for the
current moment, neglecting future moments and leading to sub-optimal or
unstable layouts over time. In this work, we present RL-LABEL, a deep
reinforcement learning-based method for managing the placement of AR labels in
scenarios involving moving objects. RL-LABEL considers the current and
predicted future states of objects and labels, such as positions and
velocities, as well as the user's viewpoint, to make informed decisions about
label placement. It balances the trade-offs between immediate and long-term
objectives. Our experiments on two real-world datasets show that RL-LABEL
effectively learns the decision-making process for long-term optimization,
outperforming two baselines (i.e., no view management and a force-based method)
by minimizing label occlusions, line intersections, and label movement
distance. Additionally, a user study involving 18 participants indicates that
RL-LABEL excels over the baselines in aiding users to identify, compare, and
summarize data on AR labels within dynamic scenes
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