Simultaneous Localization and Mapping (SLAM) is an essential component of autonomous robotic applications and self-driving vehicles, enabling them to understand and operate in their environment. Many SLAM systems have been proposed in the last decade, but they are often complex to adapt to different settings or sensor setups. In this work, we present LiDAR Graph-SLAM (LG-SLAM), a versatile range-inertial SLAM framework that can be adapted to different types of sensors and environments, from underground mines to offices with minimal parameter tuning. Our system integrates range, inertial and GNSS measurements into a graph-based optimization framework. We also use a refined submap management approach and a robust loop closure method that effectively accounts for uncertainty in the identification and validation of putative loop closures, ensuring global consistency and robustness. Enabled by a parallelized architecture and GPU integration, our system achieves pose estimation at LiDAR frame rate, along with online loop closing and graph optimization. We validate our system in diverse environments using public datasets and real-world data, consistently achieving an average error below 20 cm and outperforming other state-of-the-art algorithms.

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4\% in full force range) for normal force, and 0.095N (6.3\%) and 0.062N (4.1\%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors.

Discovery of novel and promising materials is a critical challenge in the field of chemistry and material science, traditionally approached through methodologies ranging from trial-and-error to machine learning-driven inverse design. Recent studies suggest that transformer-based language models can be utilized as material generative models to expand chemical space and explore materials with desired properties. In this work, we introduce the Catalyst Generative Pretrained Transformer (CatGPT), trained to generate string representations of inorganic catalyst structures from a vast chemical space. CatGPT not only demonstrates high performance in generating valid and accurate catalyst structures but also serves as a foundation model for generating desired types of catalysts by fine-tuning with sparse and specified datasets. As an example, we fine-tuned the pretrained CatGPT using a binary alloy catalyst dataset designed for screening two-electron oxygen reduction reaction (2e-ORR) catalyst and generate catalyst structures specialized for 2e-ORR. Our work demonstrates the potential of language models as generative tools for catalyst discovery.

Prime Day is officially over. Did your friend mention a killer deal they scored? Are you now dealing with FOMO? Well not to worry, roughly half of the Amazon Prime Day deals we highlighted during the main event are still kicking around, though they are expiring quickly. These are all products we here at WIRED have tested and recommend--some prices have slightly increased but are still a sale price, while a few have gone lower. Your next opportunity to score a good deal is around October and November, for Amazon's second Prime Day sale event and Black Friday, so take advantage, but only buy something if you actually want or need it. We test products year-round and handpicked these Prime Day deals. Products that are sold out or no longer discounted will be crossed out. We'll update this guide regularly throughout Prime Day by adding fresh deals and removing dead deals. If you buy something using links in our stories, we may earn a commission. This helps support our journalism.

Domain generalization achieves fault diagnosis on unseen modes. In process industrial systems, fault samples are limited, and only single-mode fault data can be obtained. Extracting domain-invariant fault features from single-mode data for unseen mode fault diagnosis poses challenges. Existing methods utilize a generator module to simulate samples of unseen modes. However, multi-mode samples contain complex spatiotemporal information, which brings significant difficulties to accurate sample generation. Therefore, double gradient reversal network (DGRN) is proposed. First, the model is pre-trained to acquire fault knowledge from the single seen mode. Then, pseudo-fault feature generation strategy is designed by Adaptive instance normalization, to simulate fault features of unseen mode. The dual adversarial training strategy is created to enhance the diversity of pseudo-fault features, which models unseen modes with significant distribution differences. Subsequently, domain-invariant feature extraction strategy is constructed by contrastive learning and adversarial learning. This strategy extracts common features of faults and helps multi-mode fault diagnosis. Finally, the experiments were conducted on Tennessee Eastman process and continuous stirred-tank reactor. The experiments demonstrate that DGRN achieves high classification accuracy on unseen modes while maintaining a small model size.

University of Coimbra, CEMMPRE, ARISE, Department of Mechanical Engineering, Coimbra, Portugal. Abstract Bio-inspired soft robots have already shown the ability to handle uncertainty and adapt to unstructured environments. However, their availability is partially restricted by timeconsuming, costly and highly supervised design-fabrication processes, often based on resource intensive iterative workflows. Here, we propose an integrated approach targeting the design and fabrication of pneumatic soft actuators in a single casting step. Molds and sacrificial water-soluble hollow cores are printed using fused filament fabrication (FFF). A heated water circuit accelerates the dissolution of the core's material and guarantees its complete removal from the actuator walls, while the actuator's mechanical operability is defined through finite element analysis (FEA). This enables the fabrication of actuators with non-uniform cross sections under minimal supervision, thereby reducing the number of iterations necessary during the design and fabrication processes. Three actuators capable of bending and linear motion were designed, fabricated, integrated and demonstrated as three different bio-inspired soft robots, an earthworm-inspired robot, a four-legged robot, and a robotic gripper. We demonstrate the availability, versatility and effectiveness of the proposed methods, contributing to accelerating the design and fabrication of soft robots. This study represents a step toward increasing the accessibility of soft robots to people at a lower cost. MAIN TEXT 1. Introduction Soft robots provide a freedom of movement and flexibility similar to animals' soft bodies [1-5], making them an attractive choice for innovative robot designs and applications in unstructured environments that were previously unattainable using rigid-bodied robots [6].

Recently, magnetorheological elastomers have become an interesting smart material with many new designs for robotics. A variety of applications have been built with magnetorheological elastomers, such as vibration absorbers, actuators, or grippers, showing that this material is promising for soft robotics. In this work, the novel concept of a gripper is proposed, exploring the features of a magnetorheological elastomer and permanent magnet. The gripper uses the energy of a permanent magnet to provide a self-closing gripping mechanism. The usage of flexible material enables one to hold delicate objects of various shapes. This paper presents the rolling effect of magnetorheological elastomer and permanent magnet, the design process, and the features of the soft gripper. The effectiveness of the soft gripper was validated in a series of experiments that involved lifting different objects.

WIRED's coverage of the best Amazon Prime Day deals and biggest discounts is, as they say, built different. For starters, we only include products someone from our team has personally tested and reviewed. That means you will not find flimsy fad gadgets or shoddy dupes among our recommendations. What remains is all solid stuff. You'll often find a link to a longer write-up to a review or buying guide if you want to make a fully informed buying decision. Additionally, we obsessively track prices to make sure everything on the list is a genuinely good price right now. For more on that, consult our helpful guide to shopping like a pro on Prime Day. Today is the last day of Prime Day, so you might not see some of these deals until Amazon's second Prime Day event in October or Black Friday in November. We test products year-round and handpicked these Prime Day deals. Products that are sold out or no longer discounted will be crossed out. We'll update this guide regularly throughout Prime ...

WIRED's coverage of the best Amazon Prime Day deals is, as they say, built different. For starters, we only include products someone from our team has personally tested and reviewed. That means you will not find any flimsy fad gadgets or shoddy dupes among our recommendations. What remains is all solid stuff. You'll often find a link to a longer write-up to a review or buying guide if you want to make a fully informed buying decision. Additionally, we obsessively track prices to make sure everything on the list is a genuinely good price right now. For more on that, consult our helpful guide to shopping like a pro on Prime Day. We test products year-round and handpicked these Prime Day deals. Products that are sold out or no longer discounted will be crossed out. We'll update this guide regularly throughout Prime Day by adding fresh deals and removing dead deals. Logitech makes a lot of great, functional keyboards, but the Pop Keys (9/10, WIRED Recommends) not only leverage the ...

Contextualized Image Captioning (CIC) evolves traditional image captioning into a more complex domain, necessitating the ability for multimodal reasoning. It aims to generate image captions given specific contextual information. This paper further introduces a novel domain of Controllable Contextualized Image Captioning (Ctrl-CIC). Unlike CIC, which solely relies on broad context, Ctrl-CIC accentuates a user-defined highlight, compelling the model to tailor captions that resonate with the highlighted aspects of the context. We present two approaches, Prompting-based Controller (P-Ctrl) and Recalibration-based Controller (R-Ctrl), to generate focused captions. P-Ctrl conditions the model generation on highlight by prepending captions with highlight-driven prefixes, whereas R-Ctrl tunes the model to selectively recalibrate the encoder embeddings for highlighted tokens. Additionally, we design a GPT-4V empowered evaluator to assess the quality of the controlled captions alongside standard assessment methods. Extensive experimental results demonstrate the efficient and effective controllability of our method, charting a new direction in achieving user-adaptive image captioning. Code is available at https://github.com/ShunqiM/Ctrl-CIC .