6,928 research outputs found
Multi-agents adaptive estimation and coverage control using Gaussian regression
We consider a scenario where the aim of a group of agents is to perform the
optimal coverage of a region according to a sensory function. In particular,
centroidal Voronoi partitions have to be computed. The difficulty of the task
is that the sensory function is unknown and has to be reconstructed on line
from noisy measurements. Hence, estimation and coverage needs to be performed
at the same time. We cast the problem in a Bayesian regression framework, where
the sensory function is seen as a Gaussian random field. Then, we design a set
of control inputs which try to well balance coverage and estimation, also
discussing convergence properties of the algorithm. Numerical experiments show
the effectivness of the new approach
Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal
Model-free reinforcement learning has recently been shown to be effective at
learning navigation policies from complex image input. However, these
algorithms tend to require large amounts of interaction with the environment,
which can be prohibitively costly to obtain on robots in the real world. We
present an approach for efficiently learning goal-directed navigation policies
on a mobile robot, from only a single coverage traversal of recorded data. The
navigation agent learns an effective policy over a diverse action space in a
large heterogeneous environment consisting of more than 2km of travel, through
buildings and outdoor regions that collectively exhibit large variations in
visual appearance, self-similarity, and connectivity. We compare pretrained
visual encoders that enable precomputation of visual embeddings to achieve a
throughput of tens of thousands of transitions per second at training time on a
commodity desktop computer, allowing agents to learn from millions of
trajectories of experience in a matter of hours. We propose multiple forms of
computationally efficient stochastic augmentation to enable the learned policy
to generalise beyond these precomputed embeddings, and demonstrate successful
deployment of the learned policy on the real robot without fine tuning, despite
environmental appearance differences at test time. The dataset and code
required to reproduce these results and apply the technique to other datasets
and robots is made publicly available at rl-navigation.github.io/deployable
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