Policy Transfer across Visual and Dynamics Domain Gaps via Iterative Grounding

Understand

The ability to transfer a policy from one environment to another is a promising avenue for efficient robot learning in realistic settings where task supervision is not available.

• This can allow us to take advantage of environments well suited for training, such as simulators or laboratories, to learn a policy for a real robot in a home or office.
• To succeed, such policy transfer must overcome both the visual domain gap (e.g.
• different illumination or background) and the dynamics domain gap (e.g.