A Hybrid Technique for Active SLAM Based on RPPO Model with Transfer Learning

The problem of exploration in unknown environments is still a great challenge for autonomous mobile robots due to the lack of a priori knowledge. Active Simultaneous Localization and Mapping (SLAM) is an effective method to realize obstacle avoidance and autonomous navigation. Traditional Active SLAM is usually complex to model and difficult to adapt automatically to new operating areas. This paper presents a novel Active SLAM algorithm based on Deep Reinforcement Learning (DRL). The Relational Proximal Policy Optimization (RPPO) model with deep separable convolution and data batch processing is used to predict the action strategy and generate the action plan through the acquired environment RGB images, so as to realize the autonomous collision free exploration of the environment. Meanwhile, Gmapping is applied to locate and map the environment. Then, based on Transfer Learning, Active SLAM algorithm is applied to complex unknown environments with various dynamic and static obstacles. Finally, we present several experiments to demonstrate the advantages and feasibility of the proposed Active SLAM algorithm.

The Living Space: Psychological Well-Being and Mental Health in Response to Interiors Presented in Virtual Reality

There has been a recent interest in how architecture affects mental health and psychological well-being, motivated by the fact that we spend the majority of our waking time inside and interacting with built environments. Some studies have investigated the psychological responses to indoor design parameters; for instance, contours, and proposed that curved interiors, when compared to angular ones, were aesthetically preferred and induced higher positive emotions. The present study aimed to systematically examine this hypothesis and further explore the impact of contrasting contours on affect, behavior, and cognition. We exposed 42 participants to four well-matched indoor living rooms under a free-exploration photorealistic virtual reality paradigm. We included style as an explorative second-level variable. Out of the 33 outcome variables measured, and after correcting for false discoveries, only two eventually confirmed differences in the contours analysis, in favor of angular rooms. Analysis of style primarily validated the contrast of our stimulus set, and showed significance in one other dependent variable. Results of additional analysis using the Bayesian framework were in line with those of the frequentist approach. The present results provide evidence against the hypothesis that curvature is preferred, suggesting that the psychological response to contours in a close-to-reality architectural setting could be more complex. This study, therefore, helps to communicate a more complete scientific view on the experience of interior spaces and proposes directions for necessary future research.

Total correlation explanation of toxic metal concentrations and physiological biomarkers amongst NHANES participants

IntroductionUnravelling the health effects of multiple pollutants presents scientific and computational challenges. CorEx is an unsupervised learning algorithm that can efficiently discover multiple latent factors in highly multivariate datasets. Here, we used the CorEx algorithm to perform a hypothesis free analysis of demographic, biochemical, and toxic metal biomarker data.MethodsOur data included 77 variables from 2,750 adult participants of the National Health and Nutrition Examination Survey (NHANES 2015-2016). We used an implementation of the CorEx algorithm designed to deal with the features of bioinformatic datasets including mixed data-types. Models were fit for a range of possible latent variables and the best fit model was selected as that which resulted in the largest Total Correlation (TC) after adjustment for the number of parameters. Successive layers of CorEx were run to discovered hierarchical data structure.ResultsThe CorEx algorithm identified 20 variable clusters at the first layer. For the majority clusters, the associations between variables were consistent with known associations – e.g. gender and the hormones, estradiol and testosterone were included in the first cluster; blood organic mercury and blood total mercury were grouped in cluster 4, and cluster 6 included the liver function enzymes ALT, AST and GGT. At the second layer, 3 branches of were identified reflecting hierarchical structure. The first branch included numerous physiological biomarkers and several exogenous biomarkers. The second branch included a number endogenous and exogenous variables previously associated with hypertension, while the third branch included mercury biomarkers and some related endogenous biomarkers.DiscussionWe have demonstrated the CorEx algorithm as a useful tool for hypothesis free exploration of a biomedical dataset. This work extends previous implementations of CorEx by allowing mixed data-types to be modelled and the results showed that CorEx detected meaningful hierarchical structure. CorEx may facilitate exploration of novel datasets in future.

Data-science driven autonomous process optimization

Autonomous process optimization involves the human intervention-free exploration of a range process parameters to improve responses such as product yield and selectivity. Utilizing off-the-shelf components, we develop a closed-loop system for carrying out parallel autonomous process optimization experiments in batch. Upon implementation of our system in the optimization of a stereoselective Suzuki-Miyaura coupling, we find that the definition of a set of meaningful, broad, and unbiased process parameters is the most critical aspect of successful optimization. Importantly, we discern that phosphine ligand, a categorical parameter, is vital to determination of the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing bias into the experimental design. In seeking a systematic method for selecting a diverse set of phosphine ligands, we develop a strategy that leverages computed molecular feature clustering. The resulting optimization uncovers conditions to selectively access the desired product isomer in high yield.

A realistic locomotory model of Drosophila larva for behavioral simulations.

The Drosophila larva is extensively used as model species in experiments where behavior is recorded via tracking equipment and evaluated via population-level metrics. Although larva locomotion neuromechanics have been studied in detail, no comprehensive model has been proposed for realistic simulations of foraging experiments directly comparable to tracked recordings. Here we present a virtual larva for simulating autonomous behavior, fitting empirical observations of spatial and temporal kinematics. We propose a trilayer behavior-based control architecture for larva foraging, allowing to accommodate increasingly complex behaviors. At the basic level, forward crawling and lateral bending are generated via coupled, interfering oscillatory processes under the control of an intermittency module, alternating between crawling bouts and pauses. Next, navigation in olfactory environments is achieved via active sensing and top-down modulation of bending dynamics by concentration changes. Finally, adaptation at the highest level entails associative learning. We could accurately reproduce behavioral experiments on autonomous free exploration, chemotaxis, and odor preference testing. Inter-individual variability is preserved across virtual larva populations allowing for single animal and population studies. Our model is ideally suited to interface with neural circuit models of sensation, memory formation and retrieval, and spatial navigation.

Scale free is not rare in international trade networks

Failing to consider the strong correlations between weights and topological properties in capacity-weighted networks renders test results on the scale-free property unreliable. According to the preferential attachment mechanism, existing high-degree nodes normally attract new nodes. However, in capacity-weighted networks, the weights of existing edges increase as the network grows. We propose an optimized simplification method and apply it to international trade networks. Our study covers more than 1200 product categories annually from 1995 to 2018. We find that, on average, 38%, 38% and 69% of product networks in export, import and total trade are scale-free. Furthermore, the scale-free characteristics differ depending on the technology. Counter to expectations, the exports of high-technology products are distributed worldwide rather than concentrated in a few developed countries. Our research extends the scale-free exploration of capacity-weighted networks and demonstrates that choosing appropriate filtering methods can clarify the properties of complex networks.

Response to novelty induced by change in size and complexity of familiar objects in Lister-Hooded rats, a follow-up of 2019 study

This study examines the relationship between the change in size and change in complexity of well-known/familiarized objects and exploratory activity regulation in rats. In our experiment, the rats were exposed to three types of environmental novelty in a well-familiarized chamber: (1) addition of new tunnels to the chamber, (2) increased size of a familiarized tunnel, and (3) increased complexity of the existing tunnels. The animals responded to the addition of new tunnels with a significant behavioural shift involving increased exploration of the newly installed tunnels. This effect was stable across all three test trials. The rats exposed to a change in size of the familiar object initially reacted with a behavioural shift towards the enlarged tunnel but then re-focused on the unchanged one. There was also a significant increase in the frequency of moving between the zones of the chamber. The experimental group exposed to an increased complexity of familiar objects responded with a pronounced behavioural shift towards the complex tunnel and then slightly intensified their exploration of the unchanged one. A decrease was also observed in the frequency of moving between the zones of the chamber in the first and second test trials. In the effect size analysis, no differences were found in any of the three groups, which suggests that all manipulations had similar impact. The data obtained in this study supports the view that in rats, curiosity is at least two-dimensional: activational and cognitive. The activational aspect of curiosity may be explained by novelty-related arousal processes, while the cognitive processes are activated at longer time intervals in response to more complex stimulation. The validation of this hypothesis requires further research involving manipulations with a recently standardized protocol for measuring free exploration.

Can the Hole–Board Test Predict a Rat’s Exploratory Behavior in a Free-Exploration Test?

This study focuses on the rat activity in a hole–board setting that we considered a type of exploratory behavior. The general hypothesis is based on the claim that a motivational mechanism is central to both the response to novelty in a highly familiarized environment and the activity in the hole–board apparatus. Our sample consisted of 80 experimentally naive Lister Hooded rats. All rats were tested in the hole–board apparatus. Twenty individuals with the highest hole-board scores and twenty subjects with the lowest hole–board scores subsequently underwent an established free-exploration test. In our study, the scores obtained in the hole–board test had little predictive value for the rats’ activity in the free-exploration test. Based on our previous experience in studying exploratory behavior in the free-exploration test and the data presented in this paper, we suggest that the hole–board test is not an appropriate tool for measuring exploratory behavior in laboratory rodents.

Human–Robotic Cooperative Space Exploration

Humans have always looked up at the stars and dreamed about outer space as the final frontier. The launch of the first artificial satellite—Sputnik—in 1957 by the Soviet Union and the first man on the Moon in 1969 represent significant missions in space exploration history. In 1972, Apollo 17 marked the last human program on the lunar surface. Nevertheless, several robotic spacecrafts have traveled to the Moon, such as the Soviet Luna 24 in 1976, and China’s Chang’e 4 in 2019, which was the first time a space vehicle touched down on the Moon’s far side. The international space community is currently assessing a return to the Moon in 2024 and even beyond, in the coming decades, toward the Red Planet, Mars. Robots and rovers (for example Curiosity, Philae, Rosetta, and Perseverance) will continue to play a major role in space exploration by paving the way for future long-duration missions on celestial bodies. It is still impossible to land humans on Mars or on other celestial bodies because there are significant challenges to overcome from technological and physiological perspectives. Therefore, the support of machines and artificial intelligence is essential for developing future deep space programs as well as to reach a sustainable space exploration. One can imagine a future scenario where robots and humans collaborate on the Moon’s surface or on celestial bodies to undertake scientific research, to extract and to analyze space resources for a possible in situ utilization, as well as to build sites for human habitation and work. The principles of free exploration and cooperation are core elements in the international space legal framework as mentioned in Article I of the 1967 Treaty on Principles Governing the Activities of States in the Exploration and Use of Outer Space, Including the Moon and Other Celestial Bodies. In this context of new ‘robots–humans’ cooperation, it is also necessary to consider the provisions of the 1972 Convention on the International Liability for Damage Caused by Space Objects, the 1975 Convention on Registration of Objects Launched into Outer Space, the 1968 Agreement of the Rescue of Astronauts, the Return of Astronauts and the Return of Objects Launched into Outer Space, and the 1979 Agreement Governing the Activities of States on the Moon and Other Celestial Bodies, as well as some recent international agreements signed for future Moon missions given their significant importance for space exploration.