Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Kayalibay, Baris"'
Partially-observable problems pose a trade-off between reducing costs and gathering information. They can be solved optimally by planning in belief space, but that is often prohibitively expensive. Model-predictive control (MPC) takes the alternative
Externí odkaz:
http://arxiv.org/abs/2304.10246
Autor:
Mirchev, Atanas, Kayalibay, Baris, Agha, Ahmed, van der Smagt, Patrick, Cremers, Daniel, Bayer, Justin
We introduce PRISM, a method for real-time filtering in a probabilistic generative model of agent motion and visual perception. Previous approaches either lack uncertainty estimates for the map and agent state, do not run in real-time, do not have a
Externí odkaz:
http://arxiv.org/abs/2212.02988
We introduce a method for real-time navigation and tracking with differentiably rendered world models. Learning models for control has led to impressive results in robotics and computer games, but this success has yet to be extended to vision-based n
Externí odkaz:
http://arxiv.org/abs/2201.10335
Amortised inference enables scalable learning of sequential latent-variable models (LVMs) with the evidence lower bound (ELBO). In this setting, variational posteriors are often only partially conditioned. While the true posteriors depend, e.g., on t
Externí odkaz:
http://arxiv.org/abs/2101.07046
We solve the problem of 6-DoF localisation and 3D dense reconstruction in spatial environments as approximate Bayesian inference in a deep state-space model. Our approach leverages both learning and domain knowledge from multiple-view geometry and ri
Externí odkaz:
http://arxiv.org/abs/2006.10178
Model-based approaches bear great promise for decision making of agents interacting with the physical world. In the context of spatial environments, different types of problems such as localisation, mapping, navigation or autonomous exploration are t
Externí odkaz:
http://arxiv.org/abs/1805.07206
Convolutional neural networks have been applied to a wide variety of computer vision tasks. Recent advances in semantic segmentation have enabled their application to medical image segmentation. While most CNNs use two-dimensional kernels, recent CNN
Externí odkaz:
http://arxiv.org/abs/1701.03056