Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Fu, Letian"'
Autor:
Goldberg, Andrew, Kondap, Kavish, Qiu, Tianshuang, Ma, Zehan, Fu, Letian, Kerr, Justin, Huang, Huang, Chen, Kaiyuan, Fang, Kuan, Goldberg, Ken
Generative AI systems have shown impressive capabilities in creating text, code, and images. Inspired by the rich history of research in industrial ''Design for Assembly'', we introduce a novel problem: Generative Design-for-Robot-Assembly (GDfRA). T
Externí odkaz:
http://arxiv.org/abs/2409.17126
Autor:
Fu, Letian, Huang, Huang, Datta, Gaurav, Chen, Lawrence Yunliang, Panitch, William Chung-Ho, Liu, Fangchen, Li, Hui, Goldberg, Ken
We explore how to enhance next-token prediction models to perform in-context imitation learning on a real robot, where the robot executes new tasks by interpreting contextual information provided during the input phase, without updating its underlyin
Externí odkaz:
http://arxiv.org/abs/2408.15980
Autor:
Rashid, Adam, Kim, Chung Min, Kerr, Justin, Fu, Letian, Hari, Kush, Ahmad, Ayah, Chen, Kaiyuan, Huang, Huang, Gualtieri, Marcus, Wang, Michael, Juette, Christian, Tian, Nan, Ren, Liu, Goldberg, Ken
Inventory monitoring in homes, factories, and retail stores relies on maintaining data despite objects being swapped, added, removed, or moved. We introduce Lifelong LERF, a method that allows a mobile robot with minimal compute to jointly optimize a
Externí odkaz:
http://arxiv.org/abs/2403.10494
Autor:
Fu, Letian, Datta, Gaurav, Huang, Huang, Panitch, William Chung-Ho, Drake, Jaimyn, Ortiz, Joseph, Mukadam, Mustafa, Lambeta, Mike, Calandra, Roberto, Goldberg, Ken
Touch is an important sensing modality for humans, but it has not yet been incorporated into a multimodal generative language model. This is partially due to the difficulty of obtaining natural language labels for tactile data and the complexity of a
Externí odkaz:
http://arxiv.org/abs/2402.13232
Autor:
Fu, Letian, Lian, Long, Wang, Renhao, Shi, Baifeng, Wang, Xudong, Yala, Adam, Darrell, Trevor, Efros, Alexei A., Goldberg, Ken
In this work, we re-examine inter-patch dependencies in the decoding mechanism of masked autoencoders (MAE). We decompose this decoding mechanism for masked patch reconstruction in MAE into self-attention and cross-attention. Our investigations sugge
Externí odkaz:
http://arxiv.org/abs/2401.14391
Autor:
Radosavovic, Ilija, Shi, Baifeng, Fu, Letian, Goldberg, Ken, Darrell, Trevor, Malik, Jitendra
We present a self-supervised sensorimotor pre-training approach for robotics. Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens. Given a sequence of camera images, proprioceptive robot states, and actions, we e
Externí odkaz:
http://arxiv.org/abs/2306.10007
Industrial insertion tasks are often performed repetitively with parts that are subject to tight tolerances and prone to breakage. Learning an industrial insertion policy in real is challenging as the collision between the parts and the environment c
Externí odkaz:
http://arxiv.org/abs/2210.01340
Autor:
Huang, Huang, Fu, Letian, Danielczuk, Michael, Kim, Chung Min, Tam, Zachary, Ichnowski, Jeffrey, Angelova, Anelia, Ichter, Brian, Goldberg, Ken
Stacking increases storage efficiency in shelves, but the lack of visibility and accessibility makes the mechanical search problem of revealing and extracting target objects difficult for robots. In this paper, we extend the lateral-access mechanical
Externí odkaz:
http://arxiv.org/abs/2207.02347
Autor:
Huang, Huang, Danielczuk, Michael, Kim, Chung Min, Fu, Letian, Tam, Zachary, Ichnowski, Jeffrey, Angelova, Anelia, Ichter, Brian, Goldberg, Ken
Shelves are common in homes, warehouses, and commercial settings due to their storage efficiency. However, this efficiency comes at the cost of reduced visibility and accessibility. When looking from a side (lateral) view of a shelf, most objects wil
Externí odkaz:
http://arxiv.org/abs/2201.08968
Autor:
Fu, Letian, Danielczuk, Michael, Balakrishna, Ashwin, Brown, Daniel S., Ichnowski, Jeffrey, Solowjow, Eugen, Goldberg, Ken
While deep learning has enabled significant progress in designing general purpose robot grasping systems, there remain objects which still pose challenges for these systems. Recent work on Exploratory Grasping has formalized the problem of systematic
Externí odkaz:
http://arxiv.org/abs/2111.15002