Vision-based Deep Learning Model for Guiding Multi-fingered Robotic Grasping

Grasping is an area where humans still vastly outperform robots. By leveraging recent advances in deep learning we propose a vision-based model to generate human-inspired sequences of grasping primitives suitable for transfer to multi-fingered robotic hands. The proposed model, inspired by Neural Image Captioning, consists of a convolutional and recurrent part. The convolutional part employs a pre-trained model from ILSVRC-2014 adapted to combine features from multiple points of view of a single object by using a view pooling layer. The extracted features are then used to seed Long Short Term Memory recurrent units and generate sequences of primitives that can be used to guide a sophisticated multi-fingered robotic hand during the approach leading to a grasp

The evolution of adaptive strategies to deal with variable environments

What do you do when the environment varies frequently and unpredictably? You can hedge your bets! In nature, there are two ways of being a bet-hedger. There are generalists that are not particularly good, but not particularly bad either in the various possible environments. Alternatively, individuals can hedge their bets by creating a phenotypically diverse set of offspring such that at least some of them will be fit no matter how the environment changes. We have created a simple computational model of adaptation in a variable environment and surprisingly, without any direct incentive, we observed the evolution of both strategies

Whole-genome sequencing shows the role of gene regulation in local adaptation to environmental variability

Different populations of purple sea urchins have been experiencing different magnitudes of pH variability for millions of years and as a consequence have evolved population-specific phenotypic plasticity. In this study, the whole genome of 140 purple urchins from 7 populations experiencing different pH variabilities was sequenced and set of mutations putatively involved in local adaptation were identified. We found that many of these mutations are located in genes involved in ion transmembrane transport and carbonate dehydratase activity, and enhancer regions and coding regions of transcription factors, supporting the idea that gene regulation is important in adaptation to variable environmental conditions

Whole-Genome Sequencing Reveals That Regulatory and Low Pleiotropy Variants Underlie Local Adaptation to Environmental Variability in Purple Sea Urchins

Organisms experience environments that vary across both space and time. Such environmental heterogeneity shapes standing genetic variation and may influence species’ capacity to adapt to rapid environmental change. However, we know little about the kindof genetic variation that is involved in local adaptation to environmental variability. To address this gap, we sequenced the whole genomes of 140 purple sea urchins (Strongylocentrotus purpuratus) from seven populations that vary in their degree of pH variability. Despite no evidence of global population structure, we found a suite of single-nucleotide polymorphisms (SNPs) tightly correlated with local pH variability (outlier SNPs), which were overrepresented in regions putatively involved in gene regulation (long noncoding RNA and enhancers), supporting the idea that variation in regulatory regions is important for local adaptation to variability. In addition, outliers in genes were found to be (i) enriched for biomineralization and ion homeostasis functions related to low pH response, (ii) less central to the protein-protein interaction network, and (iii) underrepresented among genes highly expressed during early development. Taken together, these results suggest that loci that underlie local adaptation to pH variability in purple sea urchins fall in regions with potentially low pleiotropic effects (based on analyses involving regulatory regions, network centrality, and expression time) involved in low pH response (based on functional enrichment)