Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Gummadi, Meghna"'
Despite outstanding semantic scene segmentation in closed-worlds, deep neural networks segment novel instances poorly, which is required for autonomous agents acting in an open world. To improve out-of-distribution (OOD) detection for segmentation, w
Externí odkaz:
http://arxiv.org/abs/2311.07578
We present CompoSuite, an open-source simulated robotic manipulation benchmark for compositional multi-task reinforcement learning (RL). Each CompoSuite task requires a particular robot arm to manipulate one individual object to achieve a task object
Externí odkaz:
http://arxiv.org/abs/2207.04136
While deep neural networks (DNNs) have achieved impressive classification performance in closed-world learning scenarios, they typically fail to generalize to unseen categories in dynamic open-world environments, in which the number of concepts is un
Externí odkaz:
http://arxiv.org/abs/2206.13720
Autor:
Ramprasad, Chethan, Saini, Divya, Del Carmen, Henry, Krasnovsky, Lev, Chandra, Rajat, Mcgregor, Ryan, Shinohara, Russell T., Eaton, Eric, Gummadi, Meghna, Mehta, Shivan, Lewis, James D.
Publikováno v:
In Gastro Hep Advances September 2024
