Cross-domain policy adaptation with dynamics alignment.

Area of Science:

• Robotics
• Machine Learning
• Artificial Intelligence

Background:

• Robotic reinforcement learning faces challenges like undefined reward functions and high training costs.
• Existing cross-domain policy transfer methods often require aligned environmental data, limiting their applicability.

Purpose of the Study:

• To develop a cross-domain dynamics alignment framework for acquiring policies in problem domains.
• To enable policy transfer from a source domain to a target domain with differing physical parameters or morphologies.
• To address limitations of previous methods by using unpaired and unaligned dynamics trajectories.

Main Methods:

• Proposed a cross-domain dynamics alignment framework.
• Introduced cross-physics-domain policy adaptation (CPD) and cross-morphology-domain policy adaptation (CMD) algorithms.
• Developed the Boltzmann TD3 (BTD3) algorithm to enhance source domain policy performance.

Main Results:

• The framework successfully learns dynamics alignment across domains with differing physical parameters and morphologies.
• CPD and CMD algorithms facilitate effective policy transfer using unpaired and unaligned data.
• BTD3 improved source domain policies, leading to better transferability.
• Experimental results demonstrated improved policies and higher rewards in problem domains, even with limited data.

Conclusions:

• The proposed framework and algorithms effectively address challenges in robotic reinforcement learning policy acquisition.
• This approach offers a viable solution for transferring policies to new environments with reduced data requirements.
• The methods show significant promise for advancing the practical application of reinforcement learning in robotics.