On the Guaranteed Almost Equivalence between Imitation Learning from Observation and Demonstration

10/16/2020
by   Zhihao Cheng, et al.
0

Imitation learning from observation (LfO) is more preferable than imitation learning from demonstration (LfD) due to the nonnecessity of expert actions when reconstructing the expert policy from the expert data. However, previous studies imply that the performance of LfO is inferior to LfD by a tremendous gap, which makes it challenging to employ LfO in practice. By contrast, this paper proves that LfO is almost equivalent to LfD in the deterministic robot environment, and more generally even in the robot environment with bounded randomness. In the deterministic robot environment, from the perspective of the control theory, we show that the inverse dynamics disagreement between LfO and LfD approaches zero, meaning that LfO is almost equivalent to LfD. To further relax the deterministic constraint and better adapt to the practical environment, we consider bounded randomness in the robot environment and prove that the optimizing targets for both LfD and LfO remain almost same in the more generalized setting. Extensive experiments for multiple robot tasks are conducted to empirically demonstrate that LfO achieves comparable performance to LfD. In fact, most common robot systems in reality are the robot environment with bounded randomness (i.e., the environment this paper considered). Hence, our findings greatly extend the potential of LfO and suggest that we can safely apply LfO without sacrificing the performance compared to LfD in practice.

READ FULL TEXT

Authors

page 8

page 18

06/18/2019

RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration

Imitation learning has long been an approach to alleviate the tractabili...
09/27/2019

Zero-shot Imitation Learning from Demonstrations for Legged Robot Visual Navigation

Imitation learning is a popular approach for training effective visual n...
05/22/2019

Imitation Learning from Video by Leveraging Proprioception

Classically, imitation learning algorithms have been developed for ideal...
10/10/2019

Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement

This paper studies Learning from Observations (LfO) for imitation learni...
05/30/2019

Recent Advances in Imitation Learning from Observation

Imitation learning is the process by which one agent tries to learn how ...
11/13/2019

Motion Reasoning for Goal-Based Imitation Learning

We address goal-based imitation learning, where the aim is to output the...
09/21/2021

Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC

We propose a demonstration-efficient strategy to compress a computationa...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.