i would like to konw what is gr-chunk and why the acc is boosting to 3.556, thanks

[fangqi-Zhu]

[StarCycle]

Hi @fangqi-Zhu,

The output of the original GR1: (s1, a1, s2, a2, s3, a3, s4, a4, ...)
The output of GR-Chunk: (s1, a1, a2, ..., aN, s2, a2, a3, ..., a_(N+1), s3, ...)

That's the only difference in the implementation.

As for why the performance will boost, there may be 2 possibilities:

• It reduces the multimodality of the whole problem. There are many possible options when the policy need to choose a single action, but there will be fewer possible options when it need to pick a future trajectory (you can see from the following 2D navigation example). This explanation comes from a nice developer in LeRobot community, but sorry I cannot recall his/her name...

• This approach simply helps the model the build an implicit world model.