On the Guaranteed Almost Equivalence Between Imitation Learning From Observation and Demonstration

Area of Science:

• Robotics
• Machine Learning
• Control Theory

Background:

• Imitation learning from observation (LfO) is preferred over imitation learning from demonstration (LfD) as it doesn't require expert actions.
• Prior research indicates a significant performance gap between LfO and LfD, limiting LfO's practical application.

Purpose of the Study:

• To demonstrate that LfO performance is comparable to LfD in deterministic and randomly varied robot environments.
• To bridge the performance gap between LfO and LfD for practical robotic applications.

Main Methods:

• Control theory analysis to compare inverse dynamics disagreement between LfO and LfD in deterministic settings.
• Mathematical proof showing optimizing targets for LfO and LfD are similar in environments with bounded randomness.
• Empirical validation through extensive experiments on multiple robot tasks.

Main Results:

• LfO and LfD exhibit near-equivalence in deterministic robot environments.
• Optimizing targets for LfO and LfD remain similar in robot environments with bounded randomness.
• Extensive experiments confirm LfO achieves performance comparable to LfD across various robot tasks.

Conclusions:

• LfO is a viable alternative to LfD, offering comparable performance without needing expert actions.
• The findings extend the applicability of LfO to real-world robotic systems, which commonly feature bounded randomness.
• Practitioners can confidently implement LfO without compromising performance relative to LfD.