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arXiv · 2611.00955

Task-Specialized Language-Conditioned Models for Fine-Grained Robotic Manipulation

Chanho Kim·Soyun Choi
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Abstract

Fine-grained robotic manipulation requires instruction grounding, robust perception, and precise contact control. We describe our submission to the MetaFine Open Competition at CoRL 2026. Five independently trained models use a common architecture with frozen DINOv2 and CLIP encoders, trainable language-conditioned feature modulation, and task-specific action heads. We 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 behavior.

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Chanho Kim, Soyun Choi. 2026-10-31. Task-Specialized Language-Conditioned Models for Fine-Grained Robotic Manipulation. https://arxiv.org/abs/2611.00955

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