This paper presents a multi-layer reproducing kernel Hilbert space (RKHS) approach for probability distribution to real and probability distribution to function regressions. The approach maps the distributions into RKHS by distribution embeddings and, then, constructs a multi-layer RKHS within which the multi-kernel distribution regression can be implemented using an existing kernel regression algorithm, such as kernel recursive least squares (KRLS). The numerical simulations on synthetic data obtained via Gaussian mixtures show that the proposed approach outperforms existing probability distribution (DR) regression algorithms by achieving smaller mean squared errors (MSEs) and requiring less training samples.

This paper presents a camera control approach for learning unknown nonlinear target dynamics by approximating information value functions using particles that represent targets' position distributions. The target dynamics are described by a non-parametric mixture model that can learn a potentially infinite number of motion patterns. Assuming that each motion pattern can be represented as a velocity field, the target behaviors can be described by a non-parametric Dirichlet process-Gaussian process (DP-GP) mixture model. The DP-GP model has been successfully applied for clustering time-invariant spatial phenomena due to its flexibility to adapt to data complexity without overfitting. A new DP-GP information value function is presented that can be used by the sensor to explore and improve the DP-GP mixture model. The optimal camera control is computed to maximize this information value function online via a computationally efficient particle-based search method. The proposed approach is demonstrated through numerical simulations and hardware experiments in the RAVEN testbed at MIT.

This paper presents a visibility-based method for planning the motion of a mobile robotic sensor with bounded field-of-view to optimally track a moving target while localizing itself. The target and robot states are estimated from online sensor measurements and a set of a priori known landmarks, using an extended Kalman filter (EKF), and thus the proposed method is applicable to robots without a global positioning system. It is shown that the problem of optimizing the target tracking and robot localization performance is equivalent to optimizing the visibility or probability of detection in the EKF framework under mild assumptions. The control law that maximizes the probability of detection for a robotic sensor with a sector-shaped field-of-view (FoV) is derived as a function of the robot heading and aperture. Simulations have been conducted on synthetic experiments and the results show that the optimized-visibility approach is effective at avoiding target loss, and outperforms a state-of-the-art potential method based on robot trailer models [1].

This paper presents an information potential method for integrated path planning and control. The method is applicable to unicycle robotic sensors deployed to classify multiple targets in an obstacle-populated environment. A new navigation function, referred to as information potential, is generated from the target conditional mutual information, and used to design a closed-loop stable switched control law. The information potential is shown to obey the properties of potential navigation functions and to enable measurements that maximize the information value over time. The information potential is also used to construct a local roadmap for escaping local minima. The properties and computational complexity of the local roadmap algorithm are analyzed. Numerical simulation results show that the method outperforms other strategies, such as rapidly exploring random trees and classical potential field methods.

MATLAB is a popular computational system and programming environment that is used in numerous engineering and science programs in the United States. One feature of MATLAB is the capability to generate 3D visualizations, which can be used to visualize scientific data or even to simulate engineering models and processes. Unfortunately, MATLAB provides only limited interactivity for these visualizations. As a solution to this problem, we have developed a software system that easily integrates with MATLAB scripts to provide the capability to view visualizations and interact with them in virtual reality (VR) systems. We call this system “ML2VR” and expect it will introduce more users to VR by enabling a large population of MATLAB programmers to easily transition to immersive systems. We will describe the system architecture of ML2VR and report on a successful case study involving the use of ML2VR.

In some virtual reality (VR) systems, OpenGL intercept methods are used to capture and render a desktop application's OpenGL calls within an immersive display. These systems often suffer from lower frame rates due to network bandwidth limitations, implementation of the intercept routine, and in some cases, the intercepted application's frame rate. To mitigate these issues and to enhance intercept-based systems in other ways, we present intercept tags, which are OpenGL geometries that are interpreted instead of rendered. We have identified and developed several uses for intercept tags, including hand-off interactions, display techniques, and visual enhancements. To demonstrate the value of intercept tags, we conducted a user study to compare a simple virtual hand technique implemented with and without intercept tags. Our results show that intercept tags significantly improve user performance and experience.