Real-Time Grasp Detection Using Convolutional Neural NetworksPDF arXiv
The paper applied convolutional neural network to identify graspable parts. The paper is overall well organized and easy to read. The main contribution claimed in the paper is the 88% accuracy on Cornell Grasp Detection Database at a 13 fps processing rate.
- The proposed method was compared with the recent works [1,2], and the authors argued for a significant performance improvement. However, to the reviewer, the proposed method is inferior to the latest work from the same group as in [1,2].
[*] Deep Learning for Detecting Robotic Grasps, Ian Lenz, Honglak Lee, Ashutosh Saxena. To appear in International Journal of Robotics Research (IJRR), 2014. http://pr.cs.cornell.edu/deepgrasping/
In the work [*], the accuracy of baseline methods is almost over 90%. The reviewer noticed that the cited performance in Table 1 is different from that in [*]. Are they tested on different databases? Please clarify.
Though real-time performance is one of the great merits, the method in this paper and that in [*] are essentially neural networks. So the reviewer considers that this advantage is just a tradeoff between accuracy and model complexity.
MultiGrasp detection is a contribution of this paper. There are multiple graspable points on most of the objects in the database, and the database has multiple labelled ground-truth grasps. How are the direct regression and MultiGrasp models evaluated with reference to the database if the number of output is different?
This is a very well written paper that seems to make a significant contribution to grasping and manipulation by dramatically increasing the speed and accuracy of grasp detection. Below are some specific comments and suggestions, but overall the paper is interesting and important to the field.
“we implicitly assume that a good 2-D grasp can be projected back to 3-D and executed by a robot viewing the scene”
It would be interesting to further explore and discuss how the method could be adapted to estimate the full gripper pose, rather than a 2D projection of the pose.
The description of the 3 methods is clean and precise but still basically impenetrable by someone with little experience with CNN’s. Perhaps this is ultimately unavoidable, but this reader would appreciate some extra effort to make the descriptions more novice-friendly. The following sentences in particular seemed overburdened with jargon:
“Our network has five convolutional layers followed by three fully connected layers. The convolutional layers are interspersed with normalization and maxpooling layers at various stages.”
“For each fold of cross-validation, we train each model for 25 epochs. We use a learning rate of 0.0005 across all layers and a weight decay of 0.001. In the hidden layers between fully connected layers we use dropout with a probability of 0.5 as an added form of regularization.”
In the chart in table 1, it is not clear how the numbers for Jiang and Lenz are obtained. Are they from the authors’ own implementation of these algorithms? If so, the details of the implementation undoubtedly affect processing speed.
This reader would also be interested to know more about the tradeoffs of using a GPU for grasp detection in practice. Using a GPU is obviously advantageous for training and testing in this paper (processing a large batch of independent images is massively parallel). But the case of a real robot seeking to grasp a single nearby object doesn’t seem very parallel. In this case, it would seem that the thing that matters is how quickly a single image can be processed, so a high-speed CPU would be more appropriate. Perhaps the MultiGrasp algorithm itself has parallelizable tasks within the processing of one image. Please clarify if this is the case.
“our models outperforms”
This run-on sentence needs a comma: “RGB-D sensors like the Kinect are cheap and the extra depth information is invaluable for robots that interact with a 3-D environment”
One of the reviewers commented on the performance of algorithms in this paper and other two papers of the same group. I agree that discuss/clarification will make this paper stronger. The other reviewer also mentioned a few things that could help to improve the manuscripts.