Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Animesh Garg"'
Autor:
Mayank Mittal, Calvin Yu, Qinxi Yu, Jingzhou Liu, Nikita Rudin, David Hoeller, Jia Lin Yuan, Ritvik Singh, Yunrong Guo, Hammad Mazhar, Ajay Mandlekar, Buck Babich, Gavriel State, Marco Hutter, Animesh Garg
Publikováno v:
IEEE Robotics and Automation Letters, 8 (6)
We present Orbit, a unified and modular framework for robot learning powered by Nvidia Isaac Sim. It offers a modular design to easily and efficiently create robotic environments with photo-realistic scenes and high-fidelity rigid and deformable body
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::559cdfcf5b2cdff4377fd6fcc49b068b
https://doi.org/10.1109/lra.2023.3270034
https://doi.org/10.1109/lra.2023.3270034
Publikováno v:
Surgical Innovation. 30:94-102
Background. The revolutions in AI hold tremendous capacity to augment human achievements in surgery, but robust integration of deep learning algorithms with high-fidelity surgical simulation remains a challenge. We present a novel application of rein
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::297264bc1f9f2eb4207834d6cd9d995f
https://doi.org/10.1177/15533506221095298
https://doi.org/10.1177/15533506221095298
Self-driving laboratories promise to democratize automated chemical laboratories. Accurate liquid handling is an essential operation in the context of chemical labs, and consequently a self-driving laboratory will require a robotic liquid handling an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8dcdb6480f134fd53f9c566352c90ddc
https://doi.org/10.26434/chemrxiv-2023-nvxkg
https://doi.org/10.26434/chemrxiv-2023-nvxkg
Publikováno v:
Algorithmic Foundations of Robotics XV ISBN: 9783031210891
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cdd10dbde585f93c0d87976ed3e46901
https://doi.org/10.1007/978-3-031-21090-7_31
https://doi.org/10.1007/978-3-031-21090-7_31
Autor:
Chenjia Bai, Ting Xiao, Zhoufan Zhu, Lingxiao Wang, Fan Zhou, Animesh Garg, Bin He, Peng Liu, Zhaoran Wang
Publikováno v:
IEEE transactions on neural networks and learning systems.
A key challenge in offline reinforcement learning (RL) is how to ensure the learned offline policy is safe, especially in safety-critical domains. In this article, we focus on learning a distributional value function in offline RL and optimizing a wo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cb6f202986bf557db825309fe262b477
https://pubmed.ncbi.nlm.nih.gov/36331649
https://pubmed.ncbi.nlm.nih.gov/36331649
Autor:
Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg, Florian Shkurti
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 35:9666-9674
Although deep learning models have achieved state-of-the art performance on a number of vision tasks, generalization over high dimensional multi-modal data, and reliable predictive uncertainty estimation are still active areas of research. Bayesian a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ee910b2190e9b51692a94ea5a54962f7
https://doi.org/10.1609/aaai.v35i11.17163
https://doi.org/10.1609/aaai.v35i11.17163
Learning to autonomously assemble shapes is a crucial skill for many robotic applications. While the majority of existing part assembly methods focus on correctly posing semantic parts to recreate a whole object, we interpret assembly more literally:
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f9ee29b34e81006887fe89c8c296c127
http://arxiv.org/abs/2205.14886
http://arxiv.org/abs/2205.14886
Publikováno v:
IEEE Micro. 40:17-25
Deep neural networks (DNNs) frequently contain far more weights, represented at a higher precision, than are required for the specific task which they are trained to perform. Consequently, they can often be compressed using techniques such as weight
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33ff3ddc5bbf827319388fd29e93ed9b
https://doi.org/10.1109/mm.2020.3012391
https://doi.org/10.1109/mm.2020.3012391
Autor:
Peter A. Zachares, Michelle A. Lee, Matthew Tan, Animesh Garg, Krishnan Srinivasan, Jeannette Bohg, Silvio Savarese, Yuke Zhu, Li Fei-Fei
Publikováno v:
IEEE Transactions on Robotics. 36:582-596
Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. It is non-trivial to manually design a robot controller that combines these modalities which have very different characteristics. While deep r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::735927986f85d70489373811233d53f3
https://doi.org/10.1109/tro.2019.2959445
https://doi.org/10.1109/tro.2019.2959445
Robotic cutting of soft materials is critical for applications such as food processing, household automation, and surgical manipulation. As in other areas of robotics, simulators can facilitate controller verification, policy learning, and dataset ge
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::878c270d03ccc1700deb7cf2433d800f
http://arxiv.org/abs/2203.10263
http://arxiv.org/abs/2203.10263