Robot Reliability and Active Learning

This post contributes to the field of robot reliability by detailing how to achieve high success rates in complex manipulation tasks. It introduces a reward model-driven active learning cycle for targeted data collection and fine-tuning, a pre-training data pyramid approach, and a dual-model architecture combining reasoning and world action models. The work demonstrates successful deployments in real-world environments like restaurants and laundromats, achieving over 99% success rates on tasks like napkin folding and T-shirt folding without site-specific data.