Sequential Action Selection for Budgeted Localization in Robots

International audienceRecent years have seen a fast growth in the number of applications of Machine Learning algorithms from Computer Science to Robotics. Nevertheless, while most such attempts were successful in maximizing robot performance after a long learning phase, to our knowledge none of them explicitly takes into account the budget in the algorithm evaluation: e.g. budget limitation on the learning duration or on the maximum number of possible actions by the robot. In this paper we introduce an algorithm for robot spatial localization based on image classification using a sequential budgeted learning framework. This aims to allow the learning of policies under an explicit budget. In this case our model uses a constraint on the number of actions that can be used by the robot. We apply this algorithm to a localization problem on a simulated environment. Our approach enables to reduce the problem to a classification task under budget constraint. The model has been compared, on the one hand, to simple neural networks for the classification part and, on the other hand, to different techniques of policy selection. The results show that the model can effectively learn an efficient policy (i.e. alternating between sensor measurement and movement to get additional information in different positions) in order to optimize its localization performance under each tested fixed budget

Task-driven active sensing framework applied to leaf probing

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This article presents a new method for actively exploring a 3D workspace with the aim of localizing relevant regions for a given task. Our method encodes the exploration route in a multi-layer occupancy grid map. This map, together with a multiple-view estimator and a maximum-information-gain gathering approach, incrementally provide a better understanding of the scene until reaching the task termination criterion. This approach is designed to be applicable to any task entailing 3D object exploration where some previous knowledge of its approximate shape is available. Its suitability is demonstrated here for a leaf probing task using an eye-in-hand arm configuration in the context of a phenotyping application (leaf probing).Peer ReviewedPostprint (author's final draft

Adaptation of sensor morphology: an integrative view of perception from biologically inspired robotics perspective

Sensor morphology, the morphology of a sensing mechanism which plays a role of shaping the desired response from physical stimuli from surroundings to generate signals usable as sensory information, is one of the key common aspects of sensing processes. This paper presents a structured review of researches on bioinspired sensor morphology implemented in robotic systems, and discusses the fundamental design principles. Based on literature review, we propose two key arguments: first, owing to its synthetic nature, biologically inspired robotics approach is a unique and powerful methodology to understand the role of sensor morphology and how it can evolve and adapt to its task and environment. Second, a consideration of an integrative view of perception by looking into multidisciplinary and overarching mechanisms of sensor morphology adaptation across biology and engineering enables us to extract relevant design principles that are important to extend our understanding of the unfinished concepts in sensing and perceptionThis study was supported by the European Commission with the RoboSoft CA (A Coordination Action for Soft Robotics, contract #619319). SGN was supported by School of Engineering seed funding (2016), Malaysia Campus, Monash University

A comparison of decision making criteria and optimization methods for active robotic sensing

This work presents a comparison of decision making criteria and optimization methods for active sensing in robotics. Active sensing incorporates the following aspects: (i ) where to position sensors, and (ii ) how to make decisions for next actions, in order to maximize information gain and minimize costs. We concentrate on the second aspect: “Where should the robot move at the next time step?”. Pros and cons of the most often used statistical decision making strategies are discussed. Simulation results from a new multisine approach for active sensing of a nonholonomic mobile robot are given

Real Time Trajectory Optimization for Vision Based Navigation with Aerobatic Fixed-Wing Vehicles

Fixed-wing unmanned aerial vehicles (UAVS) pose advantages in energy efficiency, endurance, and speed, but also pose disadvantages in maneuverability. These maneuverability challenges can be addressed by exploiting high angle of attack maneuvers. However, navigation with fixed-wing UAVs in constrained spaces is still extremely difficult when the system state and environment are unknown. This essay investigates the use of vision sensors in autonomous navigation of aerobatic fixed-wing UAVs. Perception aware NMPC is explored through the integration of a visibility metric into the trajectory optimization problem. Additionally, a novel frontier-based NMPC method, which improves obstacle avoidance capabilities while mapping, is proposed. These methods are evaluated in a realistic real-time simulation

A role for sensory areas in coordinating active sensing motions

Active sensing, which incorporates closed-loop behavioral selection of information during sensory acquisition, is an important feature of many sensory modalities. We used the rodent whisker tactile system as a platform for studying the role cortical sensory areas play in coordinating active sensing motions. We examined head and whisker motions of freely moving mice performing a tactile search for a randomly located reward, and found that mice select from a diverse range of available active sensing strategies. In particular, mice selectively employed a strategy we term contact maintenance, where whisking is modulated to counteract head motion and sustain repeated contacts, but only when doing so is likely to be useful for obtaining reward. The context dependent selection of sensing strategies, along with the observation of whisker repositioning prior to head motion, suggests the possibility of higher level control, beyond simple reflexive mechanisms. In order to further investigate a possible role for primary somatosensory cortex (SI) in coordinating whisk-by-whisk motion, we delivered closed-loop optogenetic feedback to SI, time locked to whisker motions estimated through facial electromyography. We found that stimulation regularized whisking (increasing overall periodicity), and shifted whisking frequency, changes that emulate behaviors of rodents actively contacting objects. Importantly, we observed changes to whisk timing only for stimulation locked to whisker protractions, possibly encoding that natural contacts are more likely during forward motion of the whiskers. Simultaneous neural recordings from SI show cyclic changes in excitability, specifically that responses to excitatory stimulation locked to whisker retractions appeared suppressed in contrast to stimulation during protractions that resulted in changes to whisk timing. Both effects are evident within single whisks. These findings support a role for sensory cortex in guiding whisk-by-whisk motor outputs, but suggest a coupling that depends on behavioral context, occurring on multiple timescales. Elucidating a role for sensory cortex in motor outputs is important to understanding active sensing, and may further provide novel insights to guide the design of sensory neuroprostheses that exploit active sensing context

A comparison of decision making criteria and optimization methods for active robotic sensing

This work presents a comparison of decision making criteria and optimization methods for active sensing in robotics. Active sensing incorporates the following aspects: (i) where to position sensors, and (ii) how to make decisions for next actions, in order to maximize information gain and minimize costs. We concentrate on the second aspect: "Where should the robot move at the next time step?". Pros and cons of the most often used statistical decision making strategies are discussed. Simulation results from a new multisine approach for active sensing of a nonholonomic mobile robot are given. © Springer-Verlag Berlin Heidelberg 2003.status: publishe

Operational Decision Making under Uncertainty: Inferential, Sequential, and Adversarial Approaches

Modern security threats are characterized by a stochastic, dynamic, partially observable, and ambiguous operational environment. This dissertation addresses such complex security threats using operations research techniques for decision making under uncertainty in operations planning, analysis, and assessment. First, this research develops a new method for robust queue inference with partially observable, stochastic arrival and departure times, motivated by cybersecurity and terrorism applications. In the dynamic setting, this work develops a new variant of Markov decision processes and an algorithm for robust information collection in dynamic, partially observable and ambiguous environments, with an application to a cybersecurity detection problem. In the adversarial setting, this work presents a new application of counterfactual regret minimization and robust optimization to a multi-domain cyber and air defense problem in a partially observable environment