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Typos: 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. I agree that discuss/clarification will make this paper stronger. The other reviewer also mentioned a few things that could help to make improve the manuscripts.
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 doesnt 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.
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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. The description of the 3 methods is clean and precise but still basically impenetrable by someone with little experience with cnns. 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.0005 across all layers and a weight decay.001. In the hidden layers between fully connected layers we use dropout with a probability.5 as about 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?
deep learning for Detecting Robotic Grasps, ian Lenz, honglak lee, ashutosh Saxena. To appear in International. Journal of Robotics Research (ijrr 2014. Edu/deepgrasping in the work the accuracy of baseline methods is almost over. The reviewer noticed that the cited performance in Table 1 is different from that. Are they tested on different databases?
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.
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Mit.edu title cited by year learning depth from single monocular images a saxena, sh chung, a ng neural Information Processing Systems (nips) 18, 1161, make3d: learning 3d scene structure from a single still image a saxena, m sun, ay ng Pattern Analysis and Machine Intelligence. A saxena, j schulte, ay ng ijcai 7, cascaded classification models: Combining models for holistic scene understanding g heitz, s gould, a saxena, d koller neural Information Processing Systems (nips 641-648, learning Grasp Strategies with Partial Shape Information. A saxena, lls wong, ay ng aaai 3 (2, efficient grasping from rgbd images: learning using a new rectangle representation y jiang, s moseson, a saxena robotics and Automation (icra 2011 ieee homework international Conference on, robotic grasping of novel objects a saxena, j driemeyer,. Ieee international Conference on, towards holistic scene understanding: feedback enabled cascaded classification models c li, a kowdle, a saxena, t chen neural Information Processing Systems (nips the system can't perform the operation now. 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.
Chenxia wu, cornell UniversityVerified email at rnell. Software Engineer, googleVerified email at cornell. Dipendra misra, phd candidate, cornell UniversityVerified email at rnell. Amir Roshan Zamir, stanford, uc berkeleyverified email at stanford. Zhaoyin jia, cornell UniversityVerified email at cornell. Edu, jiemi Zhang Zhejiang UniversityVerified email at Adarsh Kowdle senior Scientist and founding team Member, perceptiveio, sales rified email at m Andrew Gallagher Software Engineer, googleVerified email at m Lawson. Candidate, mit csailverified email at csail.
UniversityVerified email at rnell. Professor of Computer Science, cornell UniversityVerified email at rnell. Thorsten joachims, professor of Computer Science, cornell UniversityVerified email at rnell. Silvio savarese, stanford UniversityVerified email at stanford. Cornell UniversityVerified email at cornell. Edu, ozan Sener, stanford UniversityVerified email at anford.
And aesthetics (with Prof. Zhaoyin jia, 2013 with Tsuhan Chen 3D Scene Understanding with Physics. The system can't perform the operation now. Citations per year, duplicate citations, the following articles are merged in dissertation Scholar. Their combined citations are counted only for the first article. Merged citations, this "Cited by" count includes citations to the following articles in Scholar. The ones marked * may be different from the article in the profile. Upload pdf, follow this author, get my own profile, hema Swetha koppula. Research Scientist, skydioverified email at rnell.
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Other coursework-based Projects, i've also created a fully rendered, realistic and interactive 3d game with a random terrain generator,. Ray tracer renderer, a multithreaded, smtp server, my own malloc, a fully functional filesystem with a multithreaded garbage collector,. MapReduce in ocaml, a, pokémon playing bot, a, mips processor, and. Other Personal Projects, i've also created a simple algorithmic trading simulator, tools to scrape the, cornell University student database,. Typeracer hack, a cornell instant course search tool called. Instudy, classic games like, hazlitt pong, tetris and, minesweeper, and a 3D Graphing utility. Ashutosh Saxena, alfred. Sloan Fellow and Microsoft Faculty fellow. PhD Students, congcong li, 2012, large-scale computer vision with Cascaded Classification Models.