Generative Attention Learning: a “GenerAL” framework for high-performance multi-fingered grasping in clutter
Author:
Funder
Division of Civil, Mechanical and Manufacturing Innovation
Division of Information and Intelligent Systems
Google
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
http://link.springer.com/content/pdf/10.1007/s10514-020-09907-y.pdf
Reference50 articles.
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3. Berenson, D., Diankov, R., Nishiwaki, K., Kagami, S., & Kuffner, J. (2007). Grasp planning in complex scenes. In 7th IEEE-RAS international conference on humanoid robots, 2007, IEEE (pp. 42–48).
4. Bohg, J., Morales, A., Asfour, T., & Kragic, D. (2014). Data-driven grasp synthesis—a survey. IEEE Transactions on Robotics, 30(2), 289–309.
5. Bousmalis, K., Irpan, A., Wohlhart, P., Bai, Y., Kelcey, M., Kalakrishnan, M., Downs, L., Ibarz, J., Pastor, P., & Konolige, K., et al. (2018). Using simulation and domain adaptation to improve efficiency of deep robotic grasping. In 2018 IEEE international conference on robotics and automation (ICRA), IEEE (pp. 4243–4250).
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