Last Week’s Work Review#
Recall our Phase 1 visual module training scheme
Our Phase 2 language module training scheme
the testing accuracy could achieve $99.0%$ But I doubt about the true performance of this model.
- the model can achieve high accuracy by just memorising the current 3D grid value as long as the incremental change is small.
- which is true for this dataset because one instruction is often paired with a small change in the environment
- I should change the current training scheme to prevent model from taking shortcuts
- local addition
- one step (no intermediate states)
- local addition
After completing language module, our next step is to build PDDL model to bridge the gap between current state to the target state described by the instruction.
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