Task-Critical Part Alignment Improves Imitation of Object Interactions
Overview
This paper presents enhancements to the DINOBot framework for imitation learning of object-object interactions. The authors integrate RANSAC for outlier rejection and refine semantic correspondence to focus on task-relevant regions. Evaluated on real-world tasks including picking, insertion, shaking, and pouring, the approach demonstrates a 13.8% improvement in task success rates compared to the baseline.
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- arXiv Robotics — research abstractsSignalTask-Critical Part Alignment Improves Imitation of Object Interactions
This paper presents enhancements to the DINOBot framework for imitation learning of object-object interactions. The authors integrate RANSAC for outlier rejection and refine semantic correspondence to focus on task-relevant regions. Evaluated on real-world tasks including picking, insertion, shaking, and pouring, the approach demonstrates a 13.8% improvement in task success rates compared to the baseline.
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