MESA: An Evaluation Framework for Compositional, Semantic, and Spatial Generalization in Robotics
Overview
This paper introduces MESA, a new evaluation framework designed to precisely measure language-conditioned policy generalization in robotics. It offers five evaluation suites that isolate in-distribution performance and four axes of generalization: spatial configuration, object instance, object category, and subtask composition. The authors also release MESA-Gen, a pipeline for generating tasks and demonstrations, instantiated on two platforms as the single-arm MESA suite and BiMESA, the first large-scale benchmark for bimanual generalization. Evaluating seven baseline methods including diffusion policies, VLM-based approaches, and pretrained VLAs, the study finds the strongest model achieves 52% on MESA and 49% on BiMESA, providing meaningful signals for future progress while maintaining reproducibility beyond simulation.
Generated from attributed reports · Updated 5 days ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
No current evidence-backed claims. Missing information remains not reported.
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalMESA: An Evaluation Framework for Compositional, Semantic, and Spatial Generalization in Robotics
This paper introduces MESA, a new evaluation framework designed to precisely measure language-conditioned policy generalization in robotics. It offers five evaluation suites that isolate in-distribution performance and four axes of generalization: spatial configuration, object instance, object category, and subtask composition. The authors also release MESA-Gen, a pipeline for generating tasks and demonstrations, instantiated on two platforms as the single-arm MESA suite and BiMESA, the first large-scale benchmark for bimanual generalization. Evaluating seven baseline methods including diffusion policies, VLM-based approaches, and pretrained VLAs, the study finds the strongest model achieves 52% on MESA and 49% on BiMESA, providing meaningful signals for future progress while maintaining reproducibility beyond simulation.
Event coverage history
Current attention 3·Peak within the comparable range 10（2026-11-05 06:00 UTC）·Change within the comparable range over 24 hours -49%
The trend compares participants observed continuously; its scope may be narrower than current attention totals. Hover or click to explore hourly observations, or use the left and right arrow keys.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).