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Asymptotically-optimal motion planners such as RRT* have been shown to incrementally approximate the shortest path between start and goal states. Once an initial solution is found, their performance can be dramatically improved by restricting subsequent samples to regions of the state space that can potentially improve the current solution. When the motion planning problem lies in a Euclidean space, this region X inf , called the informed set, can be sampled directly. However, when planning with differential constraints in non-Euclidean state spaces, no analytic solutions exists to sampling X inf directly.State-of-the-art approaches to sampling X inf in such domains such as Hierarchical Rejection Sampling (HRS) may still be slow in high-dimensional state space. This may cause the planning algorithm to spend most of its time trying to produces samples in X inf rather than explore it. In this paper, we suggest an alternative approach to produce samples in the informed set X inf for a wide range of settings. Our main insight is to recast this problem as one of sampling uniformly within the sub-level-set of an implicit non-convex function. This recasting enables us to apply Monte Carlo sampling methods, used very effectively in the Machine Learning and Optimization communities, to solve our problem. We show for a wide range of scenarios that using our sampler can accelerate the convergence rate to high-quality solutions in high-dimensional problems.

Abstract. We consider a set of team-based information tasks, meaning that the team's goals are to choose behaviors that provide or enhance information available to the team. These information tasks occur across a region of space and must be performed for a period of time. We present a Bayesian model for (a) how information flows in the world and (b) how information is altered in the world by the location and perceptions of both humans and robots. Building from this model, we specify the requirements for a robot's computational mental model of the task and the human teammate, including the need to understand where and how the human processes information in the world. The robot can use this mental model to select its behaviors to support the team objective, subject to a set of mission constraints.

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Inspired by biological eyes, silicon retinas with pixel-level processing have been developed to achieve very high-speed and high-quality image processing. Due to the limitation on the fill factor and the dimension of a silicon chip, both spatial and luminance resolutions have to be kept low. For recovering fine images from a silicon retina with a lower resolution, the authors propose a neural network model and its electronic counterpart by imposing random jitter to the sensor and collecting temporal statistics of the firing neurons. Statistical analysis shows that the scheme can enhance resolution of an image and emphasize contrast edges present in the image. It is further proved that the enhancement in luminance resolution and sharpness is a trade-off between recovering bias and variance. Therefore, jitter intensity needs to be optimized by considering the luminance distribution. The simulations illustrate its effect on the fine detail reconstruction using the proposed scheme.

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