Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Tanwani, Ajay Kumar"'
Publikováno v:
MICCAI 2022, pp. 714--724
Writing reports by analyzing medical images is error-prone for inexperienced practitioners and time consuming for experienced ones. In this work, we present RepsNet that adapts pre-trained vision and language models to interpret medical images and ge
Externí odkaz:
http://arxiv.org/abs/2209.13171
Autor:
Hoque, Ryan, Seita, Daniel, Balakrishna, Ashwin, Ganapathi, Aditya, Tanwani, Ajay Kumar, Jamali, Nawid, Yamane, Katsu, Iba, Soshi, Goldberg, Ken
Robotic fabric manipulation has applications in home robotics, textiles, senior care and surgery. Existing fabric manipulation techniques, however, are designed for specific tasks, making it difficult to generalize across different but related tasks.
Externí odkaz:
http://arxiv.org/abs/2102.09754
Autor:
Tanwani, Ajay Kumar
Generating large-scale synthetic data in simulation is a feasible alternative to collecting/labelling real data for training vision-based deep learning models, albeit the modelling inaccuracies do not generalize to the physical world. In this paper,
Externí odkaz:
http://arxiv.org/abs/2011.07589
Autor:
Tanwani, Ajay Kumar, Sermanet, Pierre, Yan, Andy, Anand, Raghav, Phielipp, Mariano, Goldberg, Ken
Learning meaningful visual representations in an embedding space can facilitate generalization in downstream tasks such as action segmentation and imitation. In this paper, we learn a motion-centric representation of surgical video demonstrations by
Externí odkaz:
http://arxiv.org/abs/2006.00545
Autor:
Hoque, Ryan, Seita, Daniel, Balakrishna, Ashwin, Ganapathi, Aditya, Tanwani, Ajay Kumar, Jamali, Nawid, Yamane, Katsu, Iba, Soshi, Goldberg, Ken
Robotic fabric manipulation has applications in home robotics, textiles, senior care and surgery. Existing fabric manipulation techniques, however, are designed for specific tasks, making it difficult to generalize across different but related tasks.
Externí odkaz:
http://arxiv.org/abs/2003.09044
Autor:
Seita, Daniel, Ganapathi, Aditya, Hoque, Ryan, Hwang, Minho, Cen, Edward, Tanwani, Ajay Kumar, Balakrishna, Ashwin, Thananjeyan, Brijen, Ichnowski, Jeffrey, Jamali, Nawid, Yamane, Katsu, Iba, Soshi, Canny, John, Goldberg, Ken
Sequential pulling policies to flatten and smooth fabrics have applications from surgery to manufacturing to home tasks such as bed making and folding clothes. Due to the complexity of fabric states and dynamics, we apply deep imitation learning to l
Externí odkaz:
http://arxiv.org/abs/1910.04854
The growing demand of industrial, automotive and service robots presents a challenge to the centralized Cloud Robotics model in terms of privacy, security, latency, bandwidth, and reliability. In this paper, we present a `Fog Robotics' approach to de
Externí odkaz:
http://arxiv.org/abs/1903.09589
Autor:
Tanwani, Ajay Kumar, Lee, Jonathan, Thananjeyan, Brijen, Laskey, Michael, Krishnan, Sanjay, Fox, Roy, Goldberg, Ken, Calinon, Sylvain
Generalizing manipulation skills to new situations requires extracting invariant patterns from demonstrations. For example, the robot needs to understand the demonstrations at a higher level while being invariant to the appearance of the objects, geo
Externí odkaz:
http://arxiv.org/abs/1811.07489
On-policy imitation learning algorithms such as DAgger evolve a robot control policy by executing it, measuring performance (loss), obtaining corrective feedback from a supervisor, and generating the next policy. As the loss between iterations can va
Externí odkaz:
http://arxiv.org/abs/1811.02184
Autor:
Seita, Daniel, Jamali, Nawid, Laskey, Michael, Tanwani, Ajay Kumar, Berenstein, Ron, Baskaran, Prakash, Iba, Soshi, Canny, John, Goldberg, Ken
A fundamental challenge in manipulating fabric for clothes folding and textiles manufacturing is computing "pick points" to effectively modify the state of an uncertain manifold. We present a supervised deep transfer learning approach to locate pick
Externí odkaz:
http://arxiv.org/abs/1809.09810