Task-Specialized Language-Conditioned Models for Fine-Grained Robotic Manipulation
Overview
This paper presents a submission to the MetaFine Open Competition at CoRL 2026, featuring five independently trained models that utilize frozen DINOv2 and CLIP encoders combined with trainable language-conditioned feature modulation. The approach improves performance through contact-aware grasp filtering, instruction-matched data, and expert correction of insertion failures, achieving an official overall score of 0.914 and ranking third among 15 teams.
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“improve demonstrations through contact-aware grasp filtering, instruction-matched data, and expert correction of insertion failures. At inference, task-dependent strategies include photometric test-time augmentation, stochastic action averaging, and uncertainty-triggered replanning. Our submission achieves an official overall score of 0.914 and ranks third among 15 teams. We analyze the models and their task-specific”
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- arXiv Robotics — research abstractsSignalTask-Specialized Language-Conditioned Models for Fine-Grained Robotic Manipulation
This paper presents a submission to the MetaFine Open Competition at CoRL 2026, featuring five independently trained models that utilize frozen DINOv2 and CLIP encoders combined with trainable language-conditioned feature modulation. The approach improves performance through contact-aware grasp filtering, instruction-matched data, and expert correction of insertion failures, achieving an official overall score of 0.914 and ranking third among 15 teams.
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