DGCM-Net: Dense Geometrical Correspondence Matching Network for Incremental Experience-Based Robotic Grasping.

This article presents a method for grasping novel objects by learning from experience. Successful attempts are remembered and then used to guide future grasps such that more reliable grasping is achieved over time. To transfer the learned experience to unseen objects, we introduce the dense geometric correspondence matching network (DGCM-Net). This applies metric learning to encode objects with similar geometry nearby in feature space. Retrieving relevant experience for an unseen object is thus a nearest neighbor search with the encoded feature maps. DGCM-Net also reconstructs 3D-3D correspondences using the view-dependent normalized object coordinate space to transform grasp configurations from retrieved samples to unseen objects. In comparison to baseline methods, our approach achieves an equivalent grasp success rate. However, the baselines are significantly improved when fusing the knowledge from experience with their grasp proposal strategy. Offline experiments with a grasping dataset highlight the capability to transfer grasps to new instances as well as to improve success rate over time from increasing experience. Lastly, by learning task-relevant grasps, our approach can prioritize grasp configurations that enable the functional use of objects

Limnological changes and chironomid-inferred summer air temperature from the Late Pleniglacial to the Early Holocene in the East Carpathians

Here we provide the first chironomid record and associated summer air-temperature (T VII ) reconstruction between ca. 16,800-9100 cal yr BP from Lake Saint Anne (SZA), situated in the Eastern Carpathians. SZA was formed by the youngest volcanic eruption of Ciomadul volcano at ca. 29,600 cal yr BP. Our main goals in this study are to test whether warming after Heinrich event 1 (H1; ca. 16,200 cal yr BP) had similar amplitude to the late glacial warming, while Younger Dryas (YD) summers remained relatively warm in this region of Europe. We found the most remarkable chironomid assemblage change with a T VII increase of ~3.5-3.8°C at ca. 16,350 cal yr BP at SZA, followed by another slight T VII increase of ~0.8-1.0°C at ca. 14,450 cal yr BP. Only very minor temperature variations were recorded between 14,450 cal yr BP and 11,700 cal yr BP, with an unexpected T VII decrease in the Early Holocene. Variations in water depth together with increasing analogue problems and paludification from ca. 14,200 cal yr BP onwards may have influenced the reliability of our paleotemperature record obtained from SZA. In addition, Sphagnum -indicated decreasing pH, and hence decreasing nutrient level, likely overrode the effect of summer air-temperature changes during the Early Holocene, and this may explain the bias in the chironomid-inferred summer air-temperature reconstruction in the Early Holocene section

Unsupervised Domain Adaptation through Inter-Modal Rotation for RGB-D Object Recognition

Unsupervised Domain Adaptation (DA) exploits the supervision of a label-rich source dataset to make predictions on an unlabeled target dataset by aligning the two data distributions. In robotics, DA is used to take advantage of automatically generated synthetic data, that come with 'free' annotation, to make effective predictions on real data. However, existing DA methods are not designed to cope with the multi-modal nature of RGB-D data, which are widely used in robotic vision. We propose a novel RGB-D DA method that reduces the synthetic-to-real domain shift by exploiting the inter-modal relation between the RGB and depth image. Our method consists of training a convolutional neural network to solve, in addition to the main recognition task, the pretext task of predicting the relative rotation between the RGB and depth image. To evaluate our method and encourage further research in this area, we define two benchmark datasets for object categorization and instance recognition. With extensive experiments, we show the benefits of leveraging the inter-modal relations for RGB-D DA. The code is available at: 'https://github.com/MRLoghmani/relative-rotation'

Road traffic and landscape characteristics predict the occurrence of native halophytes on roadside verges

Road management practices, such as winter de-icing create ideal habitats and competitive advantage for salt-tolerant species. We aimed to map the occurrences of halophytes along roads in Hungary. Furthermore, we tested factors that might play a role in the roadside occurrences of five chosen native halophytes from rare to common, we encountered during our field surveys. These were Festuca pseudovina, Limonium gmelinii subsp. hungaricum, Podospermum canum, Puccinellia distans and Spergularia media. We found, that at least one halophyte species was documented in 71% of the total sampling points. Germination experiments indicated that substrate salt concentration significantly decreased germination rates in each of the five species, but in case of L. gmelinii subsp. hungaricum, or P. distans germination occurred on extremely high salt concentrations. Traffic intensity, the presence of other halophytes at the sampling point and the presence of a given species in the surrounding landscape had a significant positive effect on the occurrence of four of the five model species. Our results suggest that the studied species are mostly in the early stage of their roadside spread, colonizing roadsides close to their native distribution ranges. The possibility of a future range expansion along roads cannot be excluded