Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Ramrakhya, Ram"'
Autor:
Elawady, Ahmad, Chhablani, Gunjan, Ramrakhya, Ram, Yadav, Karmesh, Batra, Dhruv, Kira, Zsolt, Szot, Andrew
Intelligent embodied agents need to quickly adapt to new scenarios by integrating long histories of experience into decision-making. For instance, a robot in an unfamiliar house initially wouldn't know the locations of objects needed for tasks and mi
Externí odkaz:
http://arxiv.org/abs/2410.02751
We present the Habitat-Matterport 3D Open Vocabulary Object Goal Navigation dataset (HM3D-OVON), a large-scale benchmark that broadens the scope and semantic range of prior Object Goal Navigation (ObjectNav) benchmarks. Leveraging the HM3DSem dataset
Externí odkaz:
http://arxiv.org/abs/2409.14296
Autor:
Yenamandra, Sriram, Ramachandran, Arun, Khanna, Mukul, Yadav, Karmesh, Vakil, Jay, Melnik, Andrew, Büttner, Michael, Harz, Leon, Brown, Lyon, Nandi, Gora Chand, PS, Arjun, Yadav, Gaurav Kumar, Kala, Rahul, Haschke, Robert, Luo, Yang, Zhu, Jinxin, Han, Yansen, Lu, Bingyi, Gu, Xuan, Liu, Qinyuan, Zhao, Yaping, Ye, Qiting, Dou, Chenxiao, Chua, Yansong, Kuzma, Volodymyr, Humennyy, Vladyslav, Partsey, Ruslan, Francis, Jonathan, Chaplot, Devendra Singh, Chhablani, Gunjan, Clegg, Alexander, Gervet, Theophile, Jain, Vidhi, Ramrakhya, Ram, Szot, Andrew, Wang, Austin, Yang, Tsung-Yen, Edsinger, Aaron, Kemp, Charlie, Shah, Binit, Kira, Zsolt, Batra, Dhruv, Mottaghi, Roozbeh, Bisk, Yonatan, Paxton, Chris
In order to develop robots that can effectively serve as versatile and capable home assistants, it is crucial for them to reliably perceive and interact with a wide variety of objects across diverse environments. To this end, we proposed Open Vocabul
Externí odkaz:
http://arxiv.org/abs/2407.06939
Autor:
Khanna, Mukul, Ramrakhya, Ram, Chhablani, Gunjan, Yenamandra, Sriram, Gervet, Theophile, Chang, Matthew, Kira, Zsolt, Chaplot, Devendra Singh, Batra, Dhruv, Mottaghi, Roozbeh
The Embodied AI community has made significant strides in visual navigation tasks, exploring targets from 3D coordinates, objects, language descriptions, and images. However, these navigation models often handle only a single input modality as the ta
Externí odkaz:
http://arxiv.org/abs/2404.06609
Autor:
Yadav, Karmesh, Majumdar, Arjun, Ramrakhya, Ram, Yokoyama, Naoki, Baevski, Alexei, Kira, Zsolt, Maksymets, Oleksandr, Batra, Dhruv
We present a single neural network architecture composed of task-agnostic components (ViTs, convolutions, and LSTMs) that achieves state-of-art results on both the ImageNav ("go to location in ") and ObjectNav ("find a chair") tasks wit
Externí odkaz:
http://arxiv.org/abs/2303.07798
We study ObjectGoal Navigation -- where a virtual robot situated in a new environment is asked to navigate to an object. Prior work has shown that imitation learning (IL) using behavior cloning (BC) on a dataset of human demonstrations achieves promi
Externí odkaz:
http://arxiv.org/abs/2301.07302
Autor:
Yadav, Karmesh, Ramrakhya, Ram, Ramakrishnan, Santhosh Kumar, Gervet, Theo, Turner, John, Gokaslan, Aaron, Maestre, Noah, Chang, Angel Xuan, Batra, Dhruv, Savva, Manolis, Clegg, Alexander William, Chaplot, Devendra Singh
We present the Habitat-Matterport 3D Semantics (HM3DSEM) dataset. HM3DSEM is the largest dataset of 3D real-world spaces with densely annotated semantics that is currently available to the academic community. It consists of 142,646 object instance an
Externí odkaz:
http://arxiv.org/abs/2210.05633
Autor:
Yadav, Karmesh, Ramrakhya, Ram, Majumdar, Arjun, Berges, Vincent-Pierre, Kuhar, Sachit, Batra, Dhruv, Baevski, Alexei, Maksymets, Oleksandr
How should we learn visual representations for embodied agents that must see and move? The status quo is tabula rasa in vivo, i.e. learning visual representations from scratch while also learning to move, potentially augmented with auxiliary tasks (e
Externí odkaz:
http://arxiv.org/abs/2204.13226
We present a large-scale study of imitating human demonstrations on tasks that require a virtual robot to search for objects in new environments -- (1) ObjectGoal Navigation (e.g. 'find & go to a chair') and (2) Pick&Place (e.g. 'find mug, pick mug,
Externí odkaz:
http://arxiv.org/abs/2204.03514
We present Fabrik, an online neural network editor that provides tools to visualize, edit, and share neural networks from within a browser. Fabrik provides a simple and intuitive GUI to import neural networks written in popular deep learning framewor
Externí odkaz:
http://arxiv.org/abs/1810.11649