Teaching robots to manipulate objects with human-like dexterity is a significant challenge in robotics. Researchers from New York University Tandon School of Engineering and the Robotics and AI Institute found that robots trained on structured, predictable demonstrations performed better than those trained on complex, variable data. Traditional robot-learning systems often rely on imitation learning, which can struggle with capturing fine movements in dexterous tasks. To address this, the team used motion-planning algorithms to create consistent demonstrations in physics simulations. They discovered that common planning methods produced too much variability, hindering effective learning. By developing alternative planning approaches that prioritized consistency, the researchers achieved substantial success rates in manipulation tasks. Robots trained on these consistent examples performed nearly perfectly in simulations and transferred their learned skills to physical hardware with impressive success rates. This study underscores the importance of structured training data in enhancing robotic learning capabilities, suggesting that quality may outweigh quantity in training datasets.