AI & ML
Benchmarking Robots for Everyday Environments: From Lab Experiments to Real-World Operations
arXiv cs.HC0 views
arXiv:2609.26490v2 Announce Type: replace-cross Abstract: This study presents an interdisciplinary process for benchmarking service robots in public everyday environments, addressing the gap between laboratory metrics and real-world operation at technology readiness levels 6-7. Three robots were evaluated in three use cases: outdoor park cleaning, pedestrian underpass cleaning, and interactive assistance in a public library. Over three years (2023-2025), seven benchmarking events (a consensus workshop and six on-site evaluations, two per use case) assessed the robots in increasingly realistic settings, from controlled conditions to public operation. An interdisciplinary panel of experts in robotics, human-robot interaction, safety, and economic viability consolidated use-case-specific metrics into a shared evaluation concept refined across three phases. Rather than ranking the robots, the evaluation examined how task fulfillment, interaction quality, safety, and economic viability interact in the transition from laboratory prototype to operational system. Direct quantitative comparability across the heterogeneous use cases remained limited, characteristic of benchmarking at this maturity level. The process yielded cross-case insights: increasing environmental realism introduces challenges that uniform metrics capture only in part; the interaction-quality guideline works best as a modular instrument adapted to each use case; safety was assessed by occupational-safety and product-certification experts who adapted industrial test procedures for public settings; and economic viability proved a cross-cutting dimension. We conclude that benchmarking at higher technology readiness levels is well served by a shared procedure for selecting and documenting metrics across these four categories, with safety as mandatory, rather than by a fixed set of metrics: generalizability lies in the procedure rather than in identical metrics.
Source:https://arxiv.org/abs/2609.26490PreviousAPeB: Benchmarking Personalization Ability of Large Language Model AgentsNextAligning Information Theory and Formal Verification: A Lean Formalization of Rate-Distortion Theory
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