854 research outputs found
Attention and Anticipation in Fast Visual-Inertial Navigation
We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to
estimate its state using an on-board camera and an inertial sensor, without any
prior knowledge of the external environment. We consider the case in which the
robot can allocate limited resources to VIN, due to tight computational
constraints. Therefore, we answer the following question: under limited
resources, what are the most relevant visual cues to maximize the performance
of visual-inertial navigation? Our approach has four key ingredients. First, it
is task-driven, in that the selection of the visual cues is guided by a metric
quantifying the VIN performance. Second, it exploits the notion of
anticipation, since it uses a simplified model for forward-simulation of robot
dynamics, predicting the utility of a set of visual cues over a future time
horizon. Third, it is efficient and easy to implement, since it leads to a
greedy algorithm for the selection of the most relevant visual cues. Fourth, it
provides formal performance guarantees: we leverage submodularity to prove that
the greedy selection cannot be far from the optimal (combinatorial) selection.
Simulations and real experiments on agile drones show that our approach ensures
state-of-the-art VIN performance while maintaining a lean processing time. In
the easy scenarios, our approach outperforms appearance-based feature selection
in terms of localization errors. In the most challenging scenarios, it enables
accurate visual-inertial navigation while appearance-based feature selection
fails to track robot's motion during aggressive maneuvers.Comment: 20 pages, 7 figures, 2 table
Sample-based Search Methods for Bayes-Adaptive Planning
A fundamental issue for control is acting in the face of uncertainty about the environment. Amongst other things, this induces a trade-off between exploration and exploitation. A model-based Bayesian agent optimizes its return by maintaining a posterior distribution over possible environments, and considering all possible future paths. This optimization is equivalent to solving a Markov Decision Process (MDP) whose hyperstate comprises the agent's beliefs about the environment, as well as its current state in that environment. This corresponding process is called a Bayes-Adaptive MDP (BAMDP). Even for MDPs with only a few states, it is generally intractable to solve the corresponding BAMDP exactly. Various heuristics have been devised, but those that are computationally tractable often perform indifferently, whereas those that perform well are typically so expensive as to be applicable only in small domains with limited structure. Here, we develop new tractable methods for planning in BAMDPs based on recent advances in the solution to large MDPs and general partially observable MDPs. Our algorithms are sample-based, plan online in a way that is focused on the current belief, and, critically, avoid expensive belief updates during simulations. In discrete domains, we use Monte-Carlo tree search to search forward in an aggressive manner. The derived algorithm can scale to large MDPs and provably converges to the Bayes-optimal solution asymptotically. We then consider a more general class of simulation-based methods in which approximation methods can be employed to allow value function estimates to generalize between hyperstates during search. This allows us to tackle continuous domains. We validate our approach empirically in standard domains by comparison with existing approximations. Finally, we explore Bayes-adaptive planning in environments that are modelled by rich, non-parametric probabilistic models. We demonstrate that a fully Bayesian agent can be advantageous in the exploration of complex and even infinite, structured domains
Valueābased potentials: Exploiting quantitative information regularity patterns in probabilistic graphical models
This study was jointly supported by the Spanish Ministry of Education and Science under projects PID2019-106758GB-C31 and TIN2016-77902-C3-2-P, and the European Regional Development Fund (FEDER). Funding for open access charge from Universidad de Granada/CBUA.When dealing with complex models (i.e., models with
many variables, a high degree of dependency between
variables, or many states per variable), the efficient representation
of quantitative information in probabilistic
graphical models (PGMs) is a challenging task. To address
this problem, this study introduces several new structures,
aptly named valueābased potentials (VBPs), which are
based exclusively on the values. VBPs leverage repeated
values to reduce memory requirements. In the present
paper, they are compared with some common structures,
like standard tables or unidimensional arrays, and probability
trees (PT). Like VBPs, PTs are designed to reduce
the memory space, but this is achieved only if value repetitions
correspond to contextāspecific independence
patterns (i.e., repeated values are related to consecutive
indices or configurations). VBPs are devised to overcome
this limitation. The goal of this study is to analyze the
properties of VBPs. We provide a theoretical analysis of
VBPs and use them to encode the quantitative information
of a set of wellāknown Bayesian networks, measuring
the access time to their content and the computational
time required to perform some inference tasks.Spanish Government PID2019-106758GB-C31
TIN2016-77902-C3-2-PEuropean Commissio
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