We propose a novel Parallel Monte Carlo tree search with Batched Simulations (PMBS) algorithm for accelerating long-horizon, episodic robotic planning tasks. Monte Carlo tree search (MCTS) is an effective heuristic search algorithm for solving episodic decision-making problems whose underlying search spaces are expansive. Leveraging a GPU-based large-scale simulator, PMBS introduces massive parallelism into MCTS for solving planning tasks through the batched execution of a large number of concurrent simulations, which allows for more efficient and accurate evaluations of the expected cost-to-go over large action spaces. When applied to the challenging manipulation tasks of object retrieval from clutter, PMBS achieves a speedup of over $30\times$ with an improved solution quality, in comparison to a serial MCTS implementation. We show that PMBS can be directly applied to real robot hardware with negligible sim-to-real differences. Supplementary material, including video, can be found at https://github.com/arc-l/pmbs.

Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance. It is being broadly used to monitor ecosystems, mainly for their preservation. Ever-growing reports of invasive species have affected the natural balance of ecosystems. Exotic invasive species have a critical impact when introduced into new ecosystems and may lead to the extinction of native species. In this study, we focus on Ludwigia peploides, considered by the European Union as an aquatic invasive species. Its presence can negatively impact the surrounding ecosystem and human activities such as agriculture, fishing, and navigation. Our goal was to develop a method to identify the presence of the species. We used images collected by a drone-mounted multispectral sensor to achieve this, creating our LudVision data set. To identify the targeted species on the collected images, we propose a new method for detecting Ludwigia p. in multispectral images. The method is based on existing state-of-the-art semantic segmentation methods modified to handle multispectral data. The proposed method achieved a producer's accuracy of 79.9% and a user's accuracy of 95.5%.

Robotic perception models, such as Deep Neural Networks (DNNs), are becoming more computationally intensive and there are several models being trained with accuracy and latency trade-offs. However, modern latency accuracy trade-offs largely report mean accuracy for single-step vision tasks, but there is little work showing which model to invoke for multi-step control tasks in robotics. The key challenge in a multi-step decision making is to make use of the right models at right times to accomplish the given task. That is, the accomplishment of the task with a minimum control cost and minimum perception time is a desideratum; this is known as the model selection problem. In this work, we precisely address this problem of invoking the correct sequence of perception models for multi-step control. In other words, we provide a provably optimal solution to the model selection problem by casting it as a multi-objective optimization problem balancing the control cost and perception time. The key insight obtained from our solution is how the variance of the perception models matters (not just the mean accuracy) for multi-step decision making, and to show how to use diverse perception models as a primitive for energy-efficient robotics. Further, we demonstrate our approach on a photo-realistic drone landing simulation using visual navigation in AirSim. Using our proposed policy, we achieved 38.04% lower control cost with 79.1% less perception time than other competing benchmarks.

Depth estimation is one of the key technologies in some fields such as autonomous driving and robot navigation. However, the traditional method of using a single sensor is inevitably limited by the performance of the sensor. Therefore, a precision and robust method for fusing the LiDAR and stereo cameras is proposed. This method fully combines the advantages of the LiDAR and stereo camera, which can retain the advantages of the high precision of the LiDAR and the high resolution of images respectively. Compared with the traditional stereo matching method, the texture of the object and lighting conditions have less influence on the algorithm. Firstly, the depth of the LiDAR data is converted to the disparity of the stereo camera. Because the density of the LiDAR data is relatively sparse on the y-axis, the converted disparity map is up-sampled using the interpolation method. Secondly, in order to make full use of the precise disparity map, the disparity map and stereo matching are fused to propagate the accurate disparity. Finally, the disparity map is converted to the depth map. Moreover, the converted disparity map can also increase the speed of the algorithm. We evaluate the proposed pipeline on the KITTI benchmark. The experiment demonstrates that our algorithm has higher accuracy than several classic methods.

We propose a novel deep learning model named ACLNet, for cloud segmentation from ground images. ACLNet uses both deep neural network and machine learning (ML) algorithm to extract complementary features. Specifically, it uses EfficientNet-B0 as the backbone, "`a trous spatial pyramid pooling" (ASPP) to learn at multiple receptive fields, and "global attention module" (GAM) to extract finegrained details from the image. ACLNet also uses k-means clustering to extract cloud boundaries more precisely. ACLNet is effective for both daytime and nighttime images. It provides lower error rate, higher recall and higher F1-score than state-of-art cloud segmentation models. The source-code of ACLNet is available here: https://github.com/ckmvigil/ACLNet.

Abstract-- The automation of data collection via mobile robots holds promise for increasing the efficacy of environmental investigations, but requires the system to autonomously determine how to sample the environment while avoiding obstacles. Downsampling these paths can result in feasible plans at the expense of distribution estimation accuracy. This work explores this tradeoff between distribution accuracy and path length for the boustrophedon decomposition algorithm. We quantify algorithm performance by computing metrics for accuracy and path length in a Monte-Figure 1: An example environment for autonomous sampling. These results demonstrate how intelligent deployment of the boustrophedon algorithm can effectively guide autonomous environmental sampling. These algorithms must be able to Environmental sampling is the process of extracting information appropriately cover the area of interest with measurements from a given environment by collecting measurements to estimate the underlying contaminant distribution or locate at different locations and analyzing the data. They must example, environmental sampling has been used for mineral also ensure that the robot is able to feasibly traverse the prospecting [1], characterization of algae blooms [2], and air resulting path, and therefore must reason about obstacles particle monitoring [3].

Analyzing the planet at scale with satellite imagery and machine learning is a dream that has been constantly hindered by the cost of difficult-to-access highly-representative high-resolution imagery. To remediate this, we introduce here the WorldStrat dataset. The largest and most varied such publicly available dataset, at Airbus SPOT 6/7 satellites' high resolution of up to 1.5 m/pixel, empowered by European Space Agency's Phi-Lab as part of the ESA-funded QueryPlanet project, we curate nearly 10,000 sqkm of unique locations to ensure stratified representation of all types of land-use across the world: from agriculture to ice caps, from forests to multiple urbanization densities. We also enrich those with locations typically under-represented in ML datasets: sites of humanitarian interest, illegal mining sites, and settlements of persons at risk. We temporally-match each high-resolution image with multiple low-resolution images from the freely accessible lower-resolution Sentinel-2 satellites at 10 m/pixel. We accompany this dataset with an open-source Python package to: rebuild or extend the WorldStrat dataset, train and infer baseline algorithms, and learn with abundant tutorials, all compatible with the popular EO-learn toolbox. We hereby hope to foster broad-spectrum applications of ML to satellite imagery, and possibly develop from free public low-resolution Sentinel2 imagery the same power of analysis allowed by costly private high-resolution imagery. We illustrate this specific point by training and releasing several highly compute-efficient baselines on the task of Multi-Frame Super-Resolution. High-resolution Airbus imagery is CC BY-NC, while the labels and Sentinel2 imagery are CC BY, and the source code and pre-trained models under BSD. The dataset is available at https://zenodo.org/record/6810792 and the software package at https://github.com/worldstrat/worldstrat .

The use of AI to analyze, synthesize, and evaluate pathways to achieve carbon neutrality (e.g., energy sector transition plans from fossil fuels to low-carbon technologies) and for applications in climate change mitigation-related policy more broadly. The use of AI to understand and/or alleviate the effect of climate change on economies, society, production, conflict, and international trade, and for applications in climate change adaptation-related policy more broadly. Methodologies and frameworks for assessing the climate impacts of AI technologies in general (e.g., increased computational energy demand, the effects of applications, and broader systemic effects), including strategies for measurement and reporting. Governance and policies required to align the use of AI with societal climate change goals, the UN Sustainable Development Goals, and associated ESG frameworks. The use of AI to analyze, synthesize, and evaluate pathways to achieve carbon neutrality (e.g., energy sector transition plans from fossil fuels to low-carbon technologies) and for applications in climate change mitigation-related policy more broadly.

Amazon has discounted most of Ring's Video Doorbells for Prime Day this year. The cheapest of the bunch is the 2020 Ring Video Doorbell, which is 25 percent off and down to $75. The upgraded Ring Video Doorbell 3 is $40 off and down to $160, while the latest model, the Video Doorbell 4, is $50 off and down to $170. While they all have some differences between them, each of these IoT devices do the same thing: let you see who's outside your front door at all times. The standard Video Doorbell likely has everything most people would need in a gadget like this.

These companies are leading the way in artificial intelligence. Artificial intelligence was once a far-off imagination of scientists and sci-fi enthusiasts. Now, the industry has a value just under $1 trillion and is projected to grow to $14 trillion by 2030, according to Ark Invest. That's because AI is more than just a supercomputer that can play chess and engage in small talk; companies are using AI to automate and streamline their business processes. For example, automated algorithms remove most of the posts that violate Facebook's community standards.