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Found 2 papers, 2 papers with code
OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics
The results demonstrate that OK-Robot achieves a 58. 5% success rate in open-ended pick-and-drop tasks, representing a new state-of-the-art in Open Vocabulary Mobile Manipulation (OVMM) with nearly 1. 8x the performance of prior work.
Learning to Detect Noisy Labels Using Model-Based Features
Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation.
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