arXiv subjects

Anna-Maria Meer

Publications and source records attributed to Anna-Maria Meer.

2 recordsLinked to original sources

arXiv2026

Benchmarking Robots for Everyday Environments: From Lab Experiments to Real-World Operations

This study introduces an interdisciplinary framework for benchmarking robots deployed in public environments, addressing the gap between traditional laboratory metrics and real-world benchmarking requirements. We evaluate three distinct robots across diverse use cases - outdoor park cleaning, pedestrian underpass cleaning, and interactive library assistance - each representing unique challenges in public daily life. Over a three-year benchmarking process (2023-2025) comprising seven benchmarking events, a consensus workshop and six on-site evaluations (two per use case), we utilized realistic indoor and outdoor test environments to assess not only technical performance but also the broader implications of deploying robots in unstructured, human-centric settings. An expert panel, spanning robotics, human-robot interaction, safety, and economics, systematically developed and refined an evaluation concept to analyze the transition from laboratory prototypes to operational systems. Our findings highlight critical factors for successful deployment, including task fulfillment, interaction quality, safety, and economic feasibility. This work provides actionable insights for researchers and practitioners aiming to bridge the gap between robotic innovation and real-world applicability.

cs.RO↗

arXiv2026

Ontology-Guided Diffusion for Zero-Shot Visual Sim2Real Transfer

Bridging the simulation-to-reality (sim2real) gap remains challenging as labelled real-world data is scarce. Existing diffusion-based approaches rely on unstructured prompts or statistical alignment, which do not capture the structured factors that make images look real. We introduce Ontology- Guided Diffusion (OGD), a neuro-symbolic zero-shot sim2real image translation framework that represents realism as structured knowledge. OGD decomposes realism into an ontology of interpretable traits -- such as lighting and material properties -- and encodes their relationships in a knowledge graph. From a synthetic image, OGD infers trait activations and uses a graph neural network to produce a global embedding. In parallel, a symbolic planner uses the ontology traits to compute a consistent sequence of visual edits needed to narrow the realism gap. The graph embedding conditions a pretrained instruction-guided diffusion model via cross-attention, while the planned edits are converted into a structured instruction prompt. Across benchmarks, our graph-based embeddings better distinguish real from synthetic imagery than baselines, and OGD outperforms state-of-the-art diffusion methods in sim2real image translations. Overall, OGD shows that explicitly encoding realism structure enables interpretable, data-efficient, and generalisable zero-shot sim2real transfer.

cs.CV↗