Zobrazeno 1 - 10
of 770
pro vyhledávání: '"Biza A"'
Autor:
Qian, Yaoyao, Zhu, Xupeng, Biza, Ondrej, Jiang, Shuo, Zhao, Linfeng, Huang, Haojie, Qi, Yu, Platt, Robert
Robotic grasping in cluttered environments remains a significant challenge due to occlusions and complex object arrangements. We have developed ThinkGrasp, a plug-and-play vision-language grasping system that makes use of GPT-4o's advanced contextual
Externí odkaz:
http://arxiv.org/abs/2407.11298
Sample efficiency is critical when applying learning-based methods to robotic manipulation due to the high cost of collecting expert demonstrations and the challenges of on-robot policy learning through online Reinforcement Learning (RL). Offline RL
Externí odkaz:
http://arxiv.org/abs/2406.13961
Autor:
Huang, Haojie, Schmeckpeper, Karl, Wang, Dian, Biza, Ondrej, Qian, Yaoyao, Liu, Haotian, Jia, Mingxi, Platt, Robert, Walters, Robin
Humans can imagine goal states during planning and perform actions to match those goals. In this work, we propose Imagination Policy, a novel multi-task key-frame policy network for solving high-precision pick and place tasks. Instead of learning act
Externí odkaz:
http://arxiv.org/abs/2406.11740
We introduce the concept of Automated Causal Discovery (AutoCD), defined as any system that aims to fully automate the application of causal discovery and causal reasoning methods. AutoCD's goal is to deliver all causal information that an expert hum
Externí odkaz:
http://arxiv.org/abs/2402.14481
Learning about the three-dimensional world from two-dimensional images is a fundamental problem in computer vision. An ideal neural network architecture for such tasks would leverage the fact that objects can be rotated and translated in three dimens
Externí odkaz:
http://arxiv.org/abs/2307.03704
Autor:
Biza, Ondrej, Thompson, Skye, Pagidi, Kishore Reddy, Kumar, Abhinav, van der Pol, Elise, Walters, Robin, Kipf, Thomas, van de Meent, Jan-Willem, Wong, Lawson L. S., Platt, Robert
Imitation learning of robot policies from few demonstrations is crucial in open-ended applications. We propose a new method, Interaction Warping, for learning SE(3) robotic manipulation policies from a single demonstration. We infer the 3D mesh of ea
Externí odkaz:
http://arxiv.org/abs/2306.12392
Real-world grasp detection is challenging due to the stochasticity in grasp dynamics and the noise in hardware. Ideally, the system would adapt to the real world by training directly on physical systems. However, this is generally difficult due to th
Externí odkaz:
http://arxiv.org/abs/2306.06489
Predicting the pose of objects from a single image is an important but difficult computer vision problem. Methods that predict a single point estimate do not predict the pose of objects with symmetries well and cannot represent uncertainty. Alternati
Externí odkaz:
http://arxiv.org/abs/2302.13926
Autor:
Biza, Ondrej, van Steenkiste, Sjoerd, Sajjadi, Mehdi S. M., Elsayed, Gamaleldin F., Mahendran, Aravindh, Kipf, Thomas
Automatically discovering composable abstractions from raw perceptual data is a long-standing challenge in machine learning. Recent slot-based neural networks that learn about objects in a self-supervised manner have made exciting progress in this di
Externí odkaz:
http://arxiv.org/abs/2302.04973
Multi-goal policy learning for robotic manipulation is challenging. Prior successes have used state-based representations of the objects or provided demonstration data to facilitate learning. In this paper, by hand-coding a high-level discrete repres
Externí odkaz:
http://arxiv.org/abs/2207.11313