The long-term vision for robotics is to have fully autonomous mobile robots that perceive the environment as humans do or even better. This article transfers the core ideas from human scene perception onto robot scene perception to contribute toward a holistic scene understanding of robots. The first contribution is to extensively survey and compare state-of-the-art robot scene perception approaches with neuroscience theories and studies of human perception. A step-by-step transfer of the perceptual process reveals similarities and differences between robots and humans. The second contribution represents an analysis of the status quo of holistic robot perception approaches to extract to what extent the perceptual capabilities of humans have been reached. Building on this, the gaps and potentials of robot perception are illustrated to address future research directions.

Remanufacturing, a key element of the circular economy, enables products and parts to have new life cycles through a systematic process. Initially, used products (cores) are visually inspected and categorized according to their manufacturer and variant before being disassembled and cleaned. Subsequently, parts are manually classified as directly reusable, reusable after reconditioning, or recyclable. As demand for remanufactured parts increases, automated classification becomes crucial. However, current Deep Learning (DL) methods, constrained by the scarcity of unique parts, often suffer from insufficient datasets, leading to overfitting. This research explores the effectiveness of Data-Efficient Generative Adversarial Network (DE-GAN) optimization approaches like FastGAN, APA, and InsGen in enhancing dataset diversity. These methods were evaluated against the State-of-the-Art (SOTA) Deep Convolutional Generative Adversarial Network (DCGAN) using metrics such as the Inception Score (IS), Fréchet Inception Distance (FID), and the classification accuracy of ResNet18 models trained with partially synthetic data. FastGAN achieved the lowest FID values among all models and led to a statistically significant improvement in ResNet18 classification accuracy. At a [1:1] real-to-synthetic ratio, the mean accuracy increased from 72% ± 4% (real-data-only) to 87% ± 3% (p < 0.001), and reached 94% ± 3% after hyperparameter optimization. In contrast, synthetic data generated by the SOTA DCGAN did not yield statistically significant improvements.

Understanding human actions is a crucial problem for service robots. However, the general trend in Action Recognition is developing and testing these systems on structured datasets. That's why this work presents a practical Skeleton-based Action Recognition framework which can be used in realistic scenarios. Our results show that although non-augmented and non-normalized data may yield comparable results on the test split of the dataset, it is far from being useful on another dataset which is a manually collected data.

We present the architecture of a novel platform that maintains collections of semantic digital twin of retail stores. The platform a) helps robot agents understand the retail environment and make sense of things happening around them, b) enables reasoning about digital twins and knowledge extraction from multiple semantic digital twins, and c) enables reasoning about actions, events, and predicting their outcomes.