Locomotion Policy Guided Traversability Learning using Volumetric Representations of Complex Environments

Despite the progress in legged robotic locomotion, autonomous navigation in unknown environments remains an open problem. Ideally, the navigation system utilizes the full potential of the robots' locomotion capabilities while operating within safety limits under uncertainty. The robot must sense and analyze the traversability of the surrounding terrain, which depends on the hardware, locomotion control, and terrain properties. It may contain information about the risk, energy, or time consumption needed to traverse the terrain. To avoid hand-crafted traversability cost functions we propose to collect traversability information about the robot and locomotion policy by simulating the traversal over randomly generated terrains using a physics simulator. Thousand of robots are simulated in parallel controlled by the same locomotion policy used in reality to acquire 57 years of real-world locomotion experience equivalent. For deployment on the real robot, a sparse convolutional network is trained to predict the simulated traversability cost, which is tailored to the deployed locomotion policy, from an entirely geometric representation of the environment in the form of a 3D voxel-occupancy map. This representation avoids the need for commonly used elevation maps, which are error-prone in the presence of overhanging obstacles and multi-floor or low-ceiling scenarios. The effectiveness of the proposed traversability prediction network is demonstrated for path planning for the legged robot ANYmal in various indoor and natural environments.Comment: accepted for 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022

Locomotion Policy Guided Traversability Learning using Volumetric Representations of Complex Environments

Despite the progress in legged robotic locomotion, autonomous navigation in unknown environments remains an open problem. Ideally, the navigation system utilizes the full potential of the robots' locomotion capabilities while operating within safety limits under uncertainty. The robot must sense and analyze the traversability of the surrounding terrain, which depends on the hardware, locomotion control, and terrain properties. It may contain information about the risk, energy, or time consumption needed to traverse the terrain. To avoid hand-crafted traversability cost functions we propose to collect traversability information about the robot and locomotion policy by simulating the traversal over randomly generated terrains using a physics simulator. Thousand of robots are simulated in parallel controlled by the same locomotion policy used in reality to acquire 57 years of real-world locomotion experience equivalent. For deployment on the real robot, a sparse convolutional network is trained to predict the simulated traversability cost, which is tailored to the deployed locomotion policy, from an entirely geometric representation of the environment in the form of a 3D voxel-occupancy map. This representation avoids the need for commonly used elevation maps, which are error-prone in the presence of overhanging obstacles and multi-floor or low-ceiling scenarios. The effectiveness of the proposed traversability prediction network is demonstrated for path planning for the legged robot ANYmal in various indoor and natural environments.ISSN:2153-085

Serum Expression of miR-23a-3p and miR-424-5p Indicate Specific Polycystic Ovary Syndrome Phenotypes: A Pilot Study

MicroRNAs (miRNAs) are single-stranded, non-coding RNAs that regulate mRNA expression on a post-transcriptional level. Observational studies suggest an association of serum miRNAs and polycystic ovary syndrome (PCOS), a common heterogeneous endocrinopathy characterized by hyperandrogenism (HA), oligo- or amenorrhea (OM) and polycystic ovaries. It is not known whether these miRNA profiles also differ between PCOS phenotypes. In this pilot study, we compared serum expression profiles between the four PCOS phenotypes (A–D) and analyzed them both in PCOS (all phenotypes) and in phenotypes with HA by quantitative-real-time PCR (qRT-PCR). The serum expression of miR-23a-3p was upregulated in phenotype B (n = 10) and discriminated it from phenotypes A (n = 11), C (n = 11) and D (n = 11, AUC = 0.837; 95%CI, 0.706–0.968; p = 0.006). The expression of miR-424-5p was downregulated in phenotype C (n = 11) and discriminated it from phenotypes A, B and D (AUC = 0.801; 95%CI, 0.591–1.000; p = 0.007). MiR-93-5p expression was downregulated in women with PCOS (all phenotypes, n = 42) compared to controls (n = 8; p = 0.042). Phenotypes with HA (A, B, C; n = 32) did not show differences in the analyzed expression pattern. Our data provide new insights into phenotype-specific miRNA alterations in the serum of women with PCOS. Understanding the differential hormonal and miRNA profiles across PCOS phenotypes is important to improve the pathophysiological understanding of PCOS heterogeneity

Radiogenomics: A systems biology approach to understanding genetic risk factors for radiotherapy toxicity?

Adverse reactions in normal tissue after radiotherapy (RT) limit the dose that can be given to tumour cells. Since 80% of individual variation in clinical response is estimated to be caused by patient-related factors, identifying these factors might allow prediction of patients with increased risk of developing severe reactions. While inactivation of cell renewal is considered a major cause of toxicity in early-reacting normal tissues, complex interactions involving multiple cell types, cytokines, and hypoxia seem important for late reactions. Here, we review ‘omics’ approaches such as screening of genetic polymorphisms or gene expression analysis, and assess the potential of epigenetic factors, posttranslational modification, signal transduction, and metabolism. Furthermore, functional assays have suggested possible associations with clinical risk of adverse reaction. Pathway analysis incorporating different ‘omics’ approaches may be more efficient in identifying critical pathways than pathway analysis based on single ‘omics’ data sets. Integrating these pathways with functional assays may be powerful in identifying multiple subgroups of RT patients characterized by different mechanisms. Thus ‘omics’ and functional approaches may synergize if they are integrated into radiogenomics ‘systems biology’ to facilitate the goal of individualised radiotherapy

Translation and International Reception II

International audienc