My main concerns are: - Feeding the actual pose of one arm (master) and the relative pose of the second arm (slave) with respect to the master and similarly other objects would have been more informative for the network to capture the relational dependencies at the pose level. A baseline comparison with this method would be useful to understand the dependency structure, especially to improve the performance for the second task. Adding other baselines with state-of-the-art methods in the related work would further improve the understanding of the work. - The authors discuss a few examples with the position in table tasks, but the effect of orientation is not explained. Does the approach generalize with randomly sampled orientation of the target object ? Are the orientations normalized to unit quaternions after prediction ? The authors are encouraged to show orientation errors and quantify the performance. - Adding visual pixel information from pixels would help to establish the true merits of graph attention mechanism. - 29 percent accuracy with table assembly tasks is rather low. The Euclidean distance error units in Table 1 seem very high. Are they normalized to per datapoint position errors ? If so, an error of 5 cm with HDR-IL seems unreasonably high. - It is not clear if the proposed methodology is specific to bimanual manipulation. Just using robotic manipulation could be more appropriate. - Experiments with real setup would have been useful to establish the merits of the proposed approach.
- It is not clear if the proposed methodology is specific to bimanual manipulation. Just using robotic manipulation could be more appropriate.