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The deployment of simple emergent behaviors in swarm robotics has been well-rehearsed in the literature. A recent study has shown how self-aggregation is possible in a multitask approach -- where multiple self-aggregation task instances occur concurrently in the same environment. The multitask approach poses new challenges, in special, how the dynamic of each group impacts the performance of others. So far, the multitask self-aggregation of groups of robots suffers from generating a circular formation -- that is not fully compact -- or is not fully autonomous. In this paper, we present a multitask self-aggregation where groups of homogeneous robots sort themselves into different compact clusters, relying solely on a line-of-sight sensor. Our multitask self-aggregation behavior was able to scale well and achieve a compact formation. We report scalability results from a series of simulation trials with different configurations in the number of groups and the number of robots per group. We were able to improve the multitask self-aggregation behavior performance in terms of the compactness of the clusters, keeping the proportion of clustered robots found in other studies.

Detecting road features is a key enabler for autonomous driving and localization. For instance, a reliable detection of poles which are widespread in road environments can improve localization. Modern deep learning-based perception systems need a significant amount of annotated data. Automatic annotation avoids time-consuming and costly manual annotation. Because automatic methods are prone to errors, managing annotation uncertainty is crucial to ensure a proper learning process. Fusing multiple annotation sources on the same dataset can be an efficient way to reduce the errors. This not only improves the quality of annotations, but also improves the learning of perception models. In this paper, we consider the fusion of three automatic annotation methods in images: feature projection from a high accuracy vector map combined with a lidar, image segmentation and lidar segmentation. Our experimental results demonstrate the significant benefits of multi-modal automatic annotation for pole detection through a comparative evaluation on manually annotated images. Finally, the resulting multi-modal fusion is used to fine-tune an object detection model for pole base detection using unlabeled data, showing overall improvements achieved by enhancing network specialization. The dataset is publicly available.

A Tesla Model 3 car in'Full Self-Driving' mode has been captured colliding with a bike lane barrier post, in a potential setback for Elon Musk's firm. The footage was captured during a drive in downtown San Jose, California, by a YouTuber who goes by the name AI Addict, and provides the first recorded evidence that the feature has been directly responsible for an accident. It shows the latest version of Tesla's self-driving software, Full Self-Driving (FSD) Beta version 10.10, veering the Model 3 into the bollard separating a bike lane. Even though the driver is hitting the brakes and furiously spins the steering wheel away from the obstacle, the AI-powered FSD system hits the bollard with a big thud. Worryingly, at other points in the video the Model 3 appears to run a red light and attempts to go down a railroad track and later a tram lane.

Rob Bollard, CIO at IP Australia, the government's intellectual property department, is proud to say that he heads up Australia's first fully digital service delivery agency. In just the space of four years, IP Australia has gone from receiving just 12% of its IP applications online – the rest coming through on paper – to now receiving 99.6% through digital channels. Not only his, but Bollard is overseeing the decommissioning of old systems, a move to the cloud, has implemented agile working, created a DevOps environment that focuses on continuous delivery, ensures systems are designed with the user in mind, and is even deploying AI technologies to improve experiences for employees and citizens. I got the chance to sit down with Bollard at Pega's annual user event in Las Vegas this week, as IP Australia has implemented the Pega platform as its case management system. Our vision is really to become a world-class IP office and to try to support the prosperity of Australians in the system.