An operations researcher spent two months training a robot arm to pick up a toy and drop it in a bowl, achieving 90% success in v4. Key lessons: data quality matters more than quantity, slow inference can mask model issues, and combining correction data with original data (DAgger) prevents forgetting tasks. Most challenges in robot learning are operations problems rather than algorithmic ones.