Evaluating Imitation Learning Policies for Robotic Manipulation Success
Research highlights the importance of design choices in robotic imitation learning.
This study investigates the effectiveness of imitation learning policies in robotic manipulation, particularly focusing on the grasping and insertion of RJ45 connectors using a robotic hand.
Through approximately 1,700 trials, the research identifies key design choices that significantly impact real-world performance, revealing that observation and action representations are crucial.
Key Findings
- Point-cloud policies improved success rates by 70-100 percentage points with specific representation techniques.
- Absolute action prediction enhanced insertion success by up to 70 percentage points.
- Only five offline metrics correlated with insertion success, emphasizing the need for real-robot evaluations.