Understanding crowd motion dynamics is critical to real-world applications, eg, surveillance
systems and autonomous driving. This is challenging because it requires effectively …

With the recent development of deep learning technology comes the wide use of artificial
intelligence (AI) models in various domains. AI shows good performance for definite …

Particle Filtering (PF) methods are an established class of procedures for performing
inference in non-linear state-space models. Resampling is a key ingredient of PF necessary …

Simultaneous localization and mapping (SLAM) remains challenging for a number of
downstream applications, such as visual robot navigation, because of rapid turns …

By approximating posterior distributions with weighted samples, particle filters (PFs) provide
an efficient mechanism for solving non-linear sequential state estimation problems. While …

In the present paper, we propose a decoder-free extension of Dreamer, a leading model-
based reinforcement learning (MBRL) method from pixels. Dreamer is a sample-and cost …

In recent years, the methods of loading and transporting rigid objects have become more
and more perfect. However, in the process of transportation, the shape control of deformable …

Real-world reinforcement learning tasks often involve some form of partial observability
where the observations only give a partial or noisy view of the true state of the world. Such …

When the environment is partially observable (PO), a deep reinforcement learning (RL)
agent must learn a suitable temporal representation of the entire history in addition to a …

Many problems in RL, such as meta RL, robust RL, and generalization in RL can be cast as
POMDPs. In theory, simply augmenting model-free RL with memory, such as recurrent …