Reliable precision grasping for unknown objects is a prerequisite for robots that work in the field of logistics, manufacturing and household tasks. The nature of this task requires a simultaneous solution of a mixture of sub-problems. These include estimating object properties, finding viable grasps and executing grasps without displacement. We propose to explicitly take perceptual uncertainty into account during grasp execution. The underlying object representation is a probabilistic signed distance field, which includes both signed distances to the surface and spatially interpretable variances. Based on this representation, we propose a two-stage grasp generation method, which is specifically designed for generating precision grasps. In order to evaluate the whole approach, we perform extensive real world grasping experiments on a set of hard-to-grasp objects. Our approach achieves 78% success rate and shows robustness to the placement orientation.
Summary
Oxford Nanopore Technologies' (ONT) sequencing platform offers an excellent opportunity to perform real-time analysis during sequencing. This feature allows for early insights into experimental data and accelerates a potential decision-making process for further analysis, which can be particularly relevant in the clinical context. Although some tools for the real-time analysis of DNA-sequencing data already exist, there is currently no application available for differential transcriptome data analysis designed for scientists or physicians with limited bioinformatics knowledge. Here we introduce NanopoReaTA, a user-friendly real-time analysis toolbox for RNA sequencing data from ONT. Sequencing results from a running or finished experiment are processed through an R Shiny-based graphical user interface (GUI) with an integrated Nextflow pipeline for whole transcriptome or gene-specific analyses. NanopoReaTA provides visual snapshots of a sequencing run in progress, thus enabling interactive sequencing and rapid decision-making that could also be applied to clinical cases.
Availability
Github https://github.com/AnWiercze/NanopoReaTA; Zenodo https://doi.org/10.5281/zenodo.8099825
Supplementary information
Supplementary data are available at Bioinformatics online.
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