Zobrazeno 1 - 10
of 3 406
pro vyhledávání: '"Object dynamics"'
Autor:
Gao, Jiawei, Wang, Ziqin, Xiao, Zeqi, Wang, Jingbo, Wang, Tai, Cao, Jinkun, Hu, Xiaolin, Liu, Si, Dai, Jifeng, Pang, Jiangmiao
Recent years have seen significant advancements in humanoid control, largely due to the availability of large-scale motion capture data and the application of reinforcement learning methodologies. However, many real-world tasks, such as moving large
Externí odkaz:
http://arxiv.org/abs/2406.14558
The manipulation of deformable linear objects (DLOs) via model-based control requires an accurate and computationally efficient dynamics model. Yet, data-driven DLO dynamics models require large training data sets while their predictions often do not
Externí odkaz:
http://arxiv.org/abs/2407.03476
Autor:
Kim, Chanho, Fuxin, Li
Modeling object dynamics with a neural network is an important problem with numerous applications. Most recent work has been based on graph neural networks. However, physics happens in 3D space, where geometric information potentially plays an import
Externí odkaz:
http://arxiv.org/abs/2404.06044
Accurately predicting deformable linear object (DLO) dynamics is challenging, especially when the task requires a model that is both human-interpretable and computationally efficient. In this work, we draw inspiration from the pseudo-rigid body metho
Externí odkaz:
http://arxiv.org/abs/2307.07975
We propose a novel framework for the task of object-centric video prediction, i.e., extracting the compositional structure of a video sequence, as well as modeling objects dynamics and interactions from visual observations in order to predict the fut
Externí odkaz:
http://arxiv.org/abs/2302.11850
We present a method to learn compositional multi-object dynamics models from image observations based on implicit object encoders, Neural Radiance Fields (NeRFs), and graph neural networks. NeRFs have become a popular choice for representing scenes d
Externí odkaz:
http://arxiv.org/abs/2202.11855
We aim for domestic robots to perform long-term indoor service. Under the object-level scene dynamics induced by daily human activities, a robot needs to robustly localize itself in the environment subject to scene uncertainties. Previous works have
Externí odkaz:
http://arxiv.org/abs/2110.02276
What would be the effect of locally poking a static scene? We present an approach that learns naturally-looking global articulations caused by a local manipulation at a pixel level. Training requires only videos of moving objects but no information o
Externí odkaz:
http://arxiv.org/abs/2106.11303
Autor:
Agnew, William, Domingos, Pedro
Current deep reinforcement learning (RL) approaches incorporate minimal prior knowledge about the environment, limiting computational and sample efficiency. \textit{Objects} provide a succinct and causal description of the world, and many recent work
Externí odkaz:
http://arxiv.org/abs/2003.01384
Having the ability to estimate an object's properties through interaction will enable robots to manipulate novel objects. Object's dynamics, specifically the friction and inertial parameters have only been estimated in a lab environment with precise
Externí odkaz:
http://arxiv.org/abs/2003.13165