The key challenge for learning-based autonomous systems operating in time-varying environments is to predict when the learned model may lose relevance. If the learned model loses relevance, then the autonomous system is at risk of making wrong decisions. The entropic value at risk (EVAR) is a computationally efficient and coherent risk measure that can be utilized to quantify this risk. In this paper, we present a Bayesian model and learning algorithms to predict the state-dependent EVAR of time-varying datasets. We discuss applications of EVAR to an exploration problem in which an autonomous agent has to choose a set of sensing locations in order to maximize the informativeness of the acquired data and learn a model of an underlying phenomenon of interest. We empirically demonstrate the efficacy of the presented model and learning algorithms on four real-world datasets.
Showing posts with label Conference. Show all posts
Showing posts with label Conference. Show all posts
Exploitation by Informed Exploration between Isolated Operatives for Information-theoretic Data Harvesting
We consider the problem of ferrying data between nodes of a sparsely distributed sensing network of Unattended Ground Sensors (UGS) with endurance-constrained Unmanned Aerial Systems (UAS). The sensing domain wherein the sparsely distributed UGS network is deployed is assumed to be highly nonstationary (time-varying) and noisy. This makes the dataferrying problem very complicated as the expected value-of-information at a sensing location can rapidly change. To address this issue, we present a new data ferrying algorithm termed Exploitation by Informed Exploration between Isolated Operatives (EIEIO), and show that with several reasonable assumptions and a model on the predicted accumulation of value-of-information, the problem can be simplified to a mathematical linear program. To solve the linear program, the UAS learns to anticipate regions in the sensing domain that have the highest degree of change. The degree of change, is learned using a novel implementation of a Cox Process called the Cox-Gaussian Process (CGP). Our approach does not require a priori knowledge of the sensing domain model to arrive at
an optimal UAS allocation strategy.
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| Given knowledge of the informatic content of all sampling locations, a closed path may be formed so the data-ferrying agent visits the most informative subset of data source locations. |
Gaussian Process based Subsumption of a Parasitic Control Component
Many existing control architectures assume that the main control system being designed is the only controller that governs a system's actuators. However, with the increasing availability of off-the shelf controls packages, the number of internal unadjustable control systems is increasing. Some of these control systems may behave in parasitic way by enforcing a rigid set of behaviors that could disrupt a desired system behavior. We present a control architecture that can subsume parasitic control behavior through iteratively shaping the main control command with an intelligent feed-forward term. Our architecture requires very little prior knowledge about the subsystem whose behavior is to be subsumed, rather it relies on online learned sparsified predictive Gaussian Process (GP) models. We provide rigorous quantifiable bounds relating the sparsification of the GP to the accuracy in estimating and subsuming the parasitic subsystem. The presented subsumption architecture is realized using a variant of D-Type iterative learning control (ILC) and is validated through a series of flight tests on a Parrot AR Drone 2.0 quadrotor where the quadrotor's sonar based altitude control loop's behavior of maintaining a fixed altitude over ground surfaces is subsumed through a main controller via a feed-forward term.
Adaptive Algorithms for Autonomous Data-Ferrying in Nonstationary Environments
Unattended ground sensors (UGS) in long-term distributed sensing deployments benefit greatly from the incorporation of unmanned aerial systems (UAS). For instance, the mobility of data-ferrying UAS may be leveraged to reduce the cost of communication between UGS, as well as extend the effective coverage and endurance of the distributed UGS network. Since the UAS are also limited in endurance, a UAS may only ferry data between a subset of the UGS during each sortie. This is particularly problematic for extended operations in nonstationary spatio-temporal domains, as the model obtained from the set of UGS may rapidly lose relevance. Moreover, the informativeness of, or the Value-of-Information (VoI) available at, each UGS may not be equal. Our approach, termed Exploitation by Informed Exploration between Isolated Operatives (EIEIO), learns a generative spatio-temporal model for the arrival of VoI at each UGS. Through EIEIO, we anticipate and prioritize the subset of UGS with the highest VoI for each data ferrying sortie. Furthermore, a lower bound on the requisite sampling time for homogeneous Poisson processes is leveraged to provide a bound on how many times the UAS must visit each UGS in order to learn a spatio-temporal VoI model.
Human Aware UAS Path Planning in Urban Environments using Nonstationary MDPs
A growing concern with deploying Unmanned Aerial Vehicles (UAVs) in urban environments is potential violation of human privacy, and the backlash this could entail. Therefore, there is a need for UAV path planning algorithms that minimize the likelihood of invading human privacy. Such algorithms would be useful for pipeline and agricultural survey, wildfire monitoring and other missions where surveillance of humans should be avoided. We formulate the problem of human-aware path planning as a nonstationary Markov Decision Process, and provide a novel model-based reinforcement learning solution that leverages Gaussian process clustering. Our algorithm is flexible enough to accommodate changes in human population densities, and is real-time computable, as opposed to competing approaches employing Bayesian nonparametrics. The approach is validated experimentally on a large-scale long duration experiment with both simulated and real UAVs.
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