Search Tree Pruning for Progressive Neural Architecture Search

Our neural architecture search algorithm progressively searches a tree of neural network architectures. Child nodes are created by inserting new layers determined by a transition graph into a parent network up to a maximum depth and pruned when performance is worse than its parent. This increases efficiency but makes the algorithm greedy. Simpler networks are successfully found before more complex ones that can achieve benchmark performance similar to other top-performing networks

Feature Learning for Multispectral Satellite Imagery Classification Using Neural Architecture Search

Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems. However, building deep learning models to process the data requires expert knowledge of machine learning. We introduce DELTA, a software toolkit to bridge this technical gap and make deep learning easily accessible to earth scientists. Visual feature engineering is a critical part of the machine learning lifecycle, and hence is a key area that will be automated by DELTA. Hand-engineered features can perform well, but require a cross functional team with expertise in both machine learning and the specific problem domain, which is costly in both researcher time and labor. The problem is more acute with multispectral satellite imagery, which requires considerable computational resources to process. In order to automate the feature learning process, a neural architecture search samples the space of asymmetric and symmetric autoencoders using evolutionary algorithms. Since denoising autoencoders have been shown to perform well for feature learning, the autoencoders are trained on various levels of noise and the features generated by the best performing autoencoders evaluated according to their performance on image classification tasks. The resulting features are demonstrated to be effective for Landsat-8 flood mapping, as well as benchmark datasets CIFAR10 and SVHN

Corrective Gradient Refinement for Mobile Robot Localization

Particle filters for mobile robot localization must balance computational requirements and accuracy of localization. Increasing the number of particles in a particle filter improves accuracy, but also increases the computational requirements. Hence, we investigate a different paradigm to better utilize particles than to increase their numbers. To this end, we introduce the Corrective Gradient Refinement (CGR) algorithm that uses the state space gradients of the observation model to improve accuracy while maintaining low computational requirements. We develop an observation model for mobile robot localization using point cloud sensors (LIDAR and depth cameras) with vector maps. This observation model is then used to analytically compute the state space gradients necessary for CGR. We show experimentally that the resulting complete localization algorithm is more accurate than the Sampling/Importance Resampling Monte Carlo Localization algorithm, while requiring fewer particles

Online pickup and delivery planning with transfers for mobile robots

Abstract — We have deployed a fleet of robots that pickup and deliver items requested by users in an office building. Users specify time windows in which the items should be picked up and delivered, and send in requests online. Our goal is to form a schedule which picks up and delivers the items as quickly as possible at the lowest cost. We introduce an auction-based scheduling algorithm which plans to transfer items between robots to make deliveries more efficiently. The algorithm can obey either hard or soft time constraints. We discuss how to replan in response to newly requested items, cancelled requests, delayed robots, and robot failures. We demonstrate the effectiveness of our approach through execution on robots, and examine the effect of transfers on large simulated problems. I

Astrobee Robot Software: Enabling Mobile Autonomy on the ISS

Astrobee is a new free-flyer robot designed to operate inside the International Space Station (ISS). Astrobee capabilities include markerless navigation, autonomous docking for recharge, perching on handrails to minimize power and modular payloads. Astrobee will operate without crew support, controlled by teleoperation, plan execution, or on-board third parties software. This slides presents the Astrobee Robot Software, a NASA Open-Source project, powering the Astrobee robot.The Astrobee Robot Software relies on a distributed architecture based on the Robot Operating System (ROS). We present the software approach, infrastructure required, and main software components

Automatic Boosted Flood Mapping from Satellite Data

Numerous algorithms have been proposed to map floods from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. However, most require human input to succeed, either to specify a threshold value or to manually annotate training data. We introduce a new algorithm based on Adaboost which effectively maps floods without any human input, allowing for a truly rapid and automatic response. The Adaboost algorithm combines multiple thresholds to achieve results comparable to state-of-the-art algorithms which do require human input. We evaluate Adaboost, as well as numerous previously proposed flood mapping algorithms, on multiple MODIS flood images, as well as on hundreds of non-flood MODIS lake images, demonstrating its effectiveness across a wide variety of conditions

Astrobee Robot Software: A Modern Software System for Space

Astrobee is a new free-flyer robot designed to operate inside the International Space Station (ISS). Astrobee capabilities include markerless navigation, autonomous docking for recharge, perching on handrails to minimize power and modular payloads. Astrobee will operate without crew support, controlled by teleoperation, plan execution, or on-board third parties software. This paper presents the Astrobee Robot Software, a NASA Open-Source project, powering the Astrobee robot. The Astrobee Robot Software relies on a distributed architecture based on the Robot Operating System (ROS). The software runs on three interconnected smart phone class processors. We present the software approach, infrastructure required, and main software components. The Astrobee Robot Software embrace modern software practices while respecting flight constraints. The paper concludes with the lessons learned, including examples usage of the software. Several research teams are already using the Astrobee Robot Software to develop novel projects that will fly on Astrobee

Multi-Agent Pickup And Delivery Planning With Transfers

<p>In Pickup and Delivery Problems (PDPs), mobile vehicles retrieve and deliver a set of items. The PDP is a well-studied, NP-hard problem. Examples of the PDP include mail and courier services, taxis, ridesharing services, and robots such as our own CoBots and CreBots, which retrieve and deliver items for the occupants of a building, and are the motivation for this thesis. The goal of the PDP is to find a schedule that delivers as many items as possible at the lowest cost, under various constraints such as time windows and vehicle capacities. We augment the PDP with transfers to form the PDP with Transfers (PDP-T). Instead of having a single vehicle retrieve and deliver each item, vehicles can transfer items to other vehicles (or chains of vehicles) for delivery. Allowing transfers makes lower cost solutions feasible, but increases the number of possible schedules exponentially. In this thesis, we contribute a series of algorithms to form schedules for variants of the PDP-T. We introduce the Very Large Neighborhood Search with Transfers (VLNS-T) algorithm to plan schedules for the most general version of the PDP-T, with constraints including time windows, capacities, vehicle start and end locations, maximum item transport times, and maximum vehicle route durations. We also contribute algorithms for simplified variants of the PDP-T, which take advantage of the problem structure to find solutions more quickly and more effectively than the general algorithm for specific PDP-T variants, some with provable guarantees. We also study the challenges of deploying PDP-T schedules on physical robots, and execute PDP-T schedules on the CoBots. The robots reschedule their tasks in response to new requests, delays, failures, and shared information from other robots. We also introduce the CreBots, which transfer items fully autonomously. Our PDP-T algorithms are evaluated on benchmark problems, on the CoBots, and on problems on city maps sampled from real world taxi data, demonstrating that lower cost schedules can be found with transfers.</p

Multi-Observation Sensor Resetting Localization with Ambiguous Landmarks

Successful approaches to the robot localization problem include Monte Carlo particle filters, which estimate non-parametric localization belief distributions. However, particle filters fare poorly at determining the robot’s position without a good initial hypothesis. This problem has been addressed for robots that sense visual landmarks with sensor resetting, by performing sensorbased resampling when the robot is lost. For robots that make sparse, ambiguous and noisy observations, standard sensor resetting places new location hypotheses across a wide region, in positions that may be inconsistent with previous observations. We propose Multi-Observation Sensor Resetting, where observations from multiple frames are merged to generate new hypotheses more effectively. We demonstrate experimentally in the robot soccer domain on the NAO humanoid robots that Multi-Observation Sensor Resetting converges more efficiently to the robot’s true position than standard sensor resetting, and is more robust to systematic vision errors