  Imitation learning (IL), aiming to learn optimal control policies from expert
demonstrations, has been an effective method for robot manipulation tasks.
However, previous IL methods either only use expensive expert demonstrations
and omit imperfect demonstrations or rely on interacting with the environment
and learning from online experiences. In the context of robotic manipulation,
we aim to conquer the above two challenges and propose a novel framework named
Similarity Weighted Behavior Transformer (SWBT). SWBT effectively learn from
both expert and imperfect demonstrations without interaction with environments.
We reveal that the easy-to-get imperfect demonstrations, such as forward and
inverse dynamics, significantly enhance the network by learning fruitful
information. To the best of our knowledge, we are the first to attempt to
integrate imperfect demonstrations into the offline imitation learning setting
for robot manipulation tasks. Extensive experiments on the ManiSkill2 benchmark
built on the high-fidelity Sapien simulator and real-world robotic manipulation
tasks demonstrated that the proposed method can extract better features and
improve the success rates for all tasks. Our code will be released upon
acceptance of the paper.
