Restored Action Generative Adversarial Imitation Learning from observation for robot manipulator.

Area of Science:

• Robotics
• Machine Learning
• Artificial Intelligence

Background:

• Imitation learning enables robots to learn tasks from demonstrations.
• Existing methods often require access to the demonstrator's actions, which can be difficult to obtain.
• Learning from state-only demonstrations is a challenging but desirable goal.

Purpose of the Study:

• To propose a novel imitation learning algorithm, Restored Action Generative Adversarial Imitation Learning (RAGAIL), for robot manipulation.
• To enable robots to learn from state-only demonstrations, eliminating the need for action data.
• To improve the performance and applicability of imitation learning in robotics.

Main Methods:

• Developed a RAGAIL algorithm utilizing Recurrent Generative Adversarial Networks (RGAN) for trajectory generation.
• Restored robot actions from a tracking controller using robot states and generated target trajectories.
• Trained an action policy to mimic demonstrator behavior using restored actions from state-only demonstrations.

Main Results:

• The proposed RAGAIL algorithm successfully learned robot manipulator behaviors from state-only demonstrations.
• Experimental results demonstrated improved learning performance compared to methods requiring action data.
• The method validated its effectiveness in real-world robot manipulator tasks.

Conclusions:

• RAGAIL offers a viable approach for imitation learning from state-only observations in robotics.
• The algorithm overcomes limitations of traditional methods by not requiring demonstrator action signals.
• This advancement facilitates more practical and accessible robot learning from human demonstrations.