- Wording is overly exaggerated in the conclusion: " ... our pioneering
contributions herald a new era in robotic adaptability ... ". Word choice is a bit flamboyant in multiple places in the writing.
- This paper seems to only be tackle in-distribution task-transfer where typically transfer is thought of as learning task A can help with a completely different task B.
- Additionally, object shape transfer is mentioned as one of the applications, but only object pose transfer is considered in the experiments.
- Reward function seems to be very hand-engineered. How many data points is required to fit the Q-network with the pretraining dataset? Is this dataset hard to collect?
- Is there any comparison with other works that use differentiable physics for task transfer?
- One of the claimed novelty in this work is the path planning algorithm for sampling new subtasks. Can you include more comparisons against other path planning algorithms in classical literature like RRT, A*, sampling-based methods, etc?
Typo and writing comments:
Figure 1: Sub-Task Accomplishment ...
Section 5.2 MAML: repeated the word "application"
Confusing last sentence in Section 5.1.4.
Section 5.3, why is this transfer task considered "innovative"?
- Wording is overly exaggerated in the conclusion: " ... our pioneering contributions herald a new era in robotic adaptability ... ". Word choice is a bit flamboyant in multiple places in the writing.