Robotic graspingdeep learningRGB-D multi-modal dataBaxterPR23D feature learningWe consider the problem of detecting robotic grasps in an RGB-D view of a scene containing objects. In this work, we apply a deep learning approach to solve this problem, which avoids time-consuming hand-design of ...
roboticgraspsdeeplearningdetecting机器人 Deep Learning for Detecting Robotic Grasps Ian Lenz, † Honglak Lee, ∗ and Ashutosh Saxena † † Department of Computer Science, Cornell University. ∗ Department of EECS, University of Michigan, Ann Arbor. Email: ianlenz@cs.cornell.edu, honglak@eec...
Deep Learning for Detecting Robotic Grasps We consider the problem of detecting robotic grasps in an RGB-D view of a scene containing objects. In this work, we apply a deep learning approach to solve this problem, which avoids time-consuming hand-design of features. This presents... I Lenz...
Deep learning for detecting robotic grasps (2015), I. Lenz et al. 17页 卖家[上传人]:re***.1 文档编号:446391 上传时间:2017-02-27 文档格式:PDF 文档大小:1.81MB下载文档到电脑,查找使用更方便 5 金贝 下载 还剩12页未读,继续阅读 /
Code from Robot Learning Lab, Cornell in paper Deep Learning for Detecting Robotic Grasps - mirsking/Deep_learning_for_detectin_robotic_grasps
In order to enhance the efficiency and accuracy of robots in automated production lines and address issues such as inaccurate positioning and limited real-time capabilities in robot-controlled grasping, a deep learning-based lightweight algorithm for rob
Lenz, I., Lee, H., Saxena, A.: Deep learning for detecting robotic grasps. Int. J. Robot. Res. 34(4–5), 705–724 (2015) 28. Littman, M.L., Sutton, R.S.: Predictive representations of state. In: Advances in Neural Information Processing Systems, pp. 1555–1561 (2002) 29. ...
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Deep Learning for Detecting Robotic Grasps We consider the problem of detecting robotic grasps in an RGB-D view of a scene containing objects. In this work, we apply a deep learning approach to solv... I Lenz,H Lee,A Saxena - 《International Journal of Robotics Research》 被引量: 486发表...
An approach based on learning eye–hand coordination for robotic grasping from monocular images was proposed in [13], to learn hand–eye coordination for grasping. A large CNN model was trained to predict the probability of the gripper movement which resulted in successful grasps. Another autonomous...