229,784 research outputs found
Human visual exploration reduces uncertainty about the sensed world
In previous papers, we introduced a normative scheme for scene construction and epistemic (visual) searches based upon active inference. This scheme provides a principled account of how people decide where to look, when categorising a visual scene based on its contents. In this paper, we use active inference to explain the visual searches of normal human subjects; enabling us to answer some key questions about visual foraging and salience attribution. First, we asked whether there is any evidence for ‘epistemic foraging’; i.e. exploration that resolves uncertainty about a scene. In brief, we used Bayesian model comparison to compare Markov decision process (MDP) models of scan-paths that did–and did not–contain the epistemic, uncertainty-resolving imperatives for action selection. In the course of this model comparison, we discovered that it was necessary to include non-epistemic (heuristic) policies to explain observed behaviour (e.g., a reading-like strategy that involved scanning from left to right). Despite this use of heuristic policies, model comparison showed that there is substantial evidence for epistemic foraging in the visual exploration of even simple scenes. Second, we compared MDP models that did–and did not–allow for changes in prior expectations over successive blocks of the visual search paradigm. We found that implicit prior beliefs about the speed and accuracy of visual searches changed systematically with experience. Finally, we characterised intersubject variability in terms of subject-specific prior beliefs. Specifically, we used canonical correlation analysis to see if there were any mixtures of prior expectations that could predict between-subject differences in performance; thereby establishing a quantitative link between different behavioural phenotypes and Bayesian belief updating. We demonstrated that better scene categorisation performance is consistently associated with lower reliance on heuristics; i.e., a greater use of a generative model of the scene to direct its exploration. © 2018 Mirza et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
SEMI: Self-supervised Exploration via Multisensory Incongruity
Efficient exploration is a long-standing problem in reinforcement learning.
In this work, we introduce a self-supervised exploration policy by
incentivizing the agent to maximize multisensory incongruity, which can be
measured in two aspects: perception incongruity and action incongruity. The
former represents the uncertainty in multisensory fusion model, while the
latter represents the uncertainty in an agent's policy. Specifically, an
alignment predictor is trained to detect whether multiple sensory inputs are
aligned, the error of which is used to measure perception incongruity. The
policy takes the multisensory observations with sensory-wise dropout as input
and outputs actions for exploration. The variance of actions is further used to
measure action incongruity. Our formulation allows the agent to learn skills by
exploring in a self-supervised manner without any external rewards. Besides,
our method enables the agent to learn a compact multimodal representation from
hard examples, which further improves the sample efficiency of our policy
learning. We demonstrate the efficacy of this formulation across a variety of
benchmark environments including object manipulation and audio-visual games
Risk Analysis Applied in Oil Exploration and Production
This research investigated the application of risk analysis to Oil exploration and production. Essentially, different organizations approach risk analysis from various perspectives depending on the company’s policies. Some problems were identified as the causes of poor risk analysis procedures such as wrong concepts and miscommunication by the risk analysis staff. The risk associated with investments in oil exploration and production among others include: risk of storm damage to offshore installations; risk relating to future oil and gas prices; risk of exploration or development of dry hole and environmental risk. The analysis in this work is based on the actual field data obtained from Devon Exploration and Production Inc. The Net Present Value (NPV) and the Expected Monetary Value (EMV) were computed using Excel and Visual Basic to determine the viability of these projects. Although the use of risk management techniques does not reduce the uncertainty in Oil field projects; it reduces the impact of the losses should an unfavourable event occur
Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter
Camera viewpoint selection is an important aspect of visual grasp detection,
especially in clutter where many occlusions are present. Where other approaches
use a static camera position or fixed data collection routines, our Multi-View
Picking (MVP) controller uses an active perception approach to choose
informative viewpoints based directly on a distribution of grasp pose estimates
in real time, reducing uncertainty in the grasp poses caused by clutter and
occlusions. In trials of grasping 20 objects from clutter, our MVP controller
achieves 80% grasp success, outperforming a single-viewpoint grasp detector by
12%. We also show that our approach is both more accurate and more efficient
than approaches which consider multiple fixed viewpoints.Comment: ICRA 2019 Video: https://youtu.be/Vn3vSPKlaEk Code:
https://github.com/dougsm/mvp_gras
Perception-aware Path Planning
In this paper, we give a double twist to the problem of planning under
uncertainty. State-of-the-art planners seek to minimize the localization
uncertainty by only considering the geometric structure of the scene. In this
paper, we argue that motion planning for vision-controlled robots should be
perception aware in that the robot should also favor texture-rich areas to
minimize the localization uncertainty during a goal-reaching task. Thus, we
describe how to optimally incorporate the photometric information (i.e.,
texture) of the scene, in addition to the the geometric one, to compute the
uncertainty of vision-based localization during path planning. To avoid the
caveats of feature-based localization systems (i.e., dependence on feature type
and user-defined thresholds), we use dense, direct methods. This allows us to
compute the localization uncertainty directly from the intensity values of
every pixel in the image. We also describe how to compute trajectories online,
considering also scenarios with no prior knowledge about the map. The proposed
framework is general and can easily be adapted to different robotic platforms
and scenarios. The effectiveness of our approach is demonstrated with extensive
experiments in both simulated and real-world environments using a
vision-controlled micro aerial vehicle.Comment: 16 pages, 20 figures, revised version. Conditionally accepted for
IEEE Transactions on Robotic
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