Bradleysauer7229

Climbing plants are being increasingly viewed as models for bioinspired growing robots capable of spanning voids and attaching to diverse substrates. We explore the functional traits of the climbing cactus Selenicereus setaceus (Cactaceae) from the Atlantic forest of Brazil and discuss the potential of these traits for robotics applications. The plant is capable of growing through highly unstructured habitats and attaching to variable substrates including soil, leaf litter, tree surfaces, rocks, and fine branches of tree canopies in wind-blown conditions. Stems develop highly variable cross-sectional geometries at different stages of growth. They include cylindrical basal stems, triangular climbing stems and apical star-shaped stems searching for supports. Searcher stems develop relatively rigid properties for a given cross-sectional area and are capable of spanning voids of up to 1 m. Optimization of rigidity in searcher stems provide some potential design ideas for additive engineering technologies where climbing robotic artifacts must limit materials and mass for curbing bending moments and buckling while climbing and searching. A two-step attachment mechanism involves deployment of recurved, multi-angled spines that grapple on to wide ranging surfaces holding the stem in place for more solid attachment via root growth from the stem. The cactus is an instructive example of how light mass searchers with a winged profile and two step attachment strategies can facilitate traversing voids and making reliable attachment to a wide range of supports and surfaces.It has been proposed that machine learning techniques can benefit from symbolic representations and reasoning systems. We describe a method in which the two can be combined in a natural and direct way by use of hyperdimensional vectors and hyperdimensional computing. By using hashing neural networks to produce binary vector representations of images, we show how hyperdimensional vectors can be constructed such that vector-symbolic inference arises naturally out of their output. ON-01910 price We design the Hyperdimensional Inference Layer (HIL) to facilitate this process and evaluate its performance compared to baseline hashing networks. In addition to this, we show that separate network outputs can directly be fused at the vector symbolic level within HILs to improve performance and robustness of the overall model. Furthermore, to the best of our knowledge, this is the first instance in which meaningful hyperdimensional representations of images are created on real data, while still maintaining hyperdimensionality.Media influence people's perceptions of reality broadly and of technology in particular. Robot villains and heroes-from Ultron to Wall-E-have been shown to serve a specific cultivation function, shaping people's perceptions of those embodied social technologies, especially when individuals do not have direct experience with them. To date, however, little is understood about the nature of the conceptions people hold for what robots are, how they work, and how they may function in society, as well as the media antecedents and relational effects of those cognitive structures. This study takes a step toward bridging that gap by exploring relationships among individuals' recall of robot characters from popular media, their mental models for actual robots, and social evaluations of an actual robot. Findings indicate that mental models consist of a small set of common and tightly linked components (beyond which there is a good deal of individual difference), but robot character recall and evaluation have little association with whether people hold any of those components. Instead, data are interpreted to suggest that cumulative sympathetic evaluations of robot media characters may form heuristics that are primed by and engaged in social evaluations of actual robots, while technical content in mental models is associated with a more utilitarian approach to actual robots.Producing feasible motions for highly redundant robots, such as humanoids, is a complicated and high-dimensional problem. Model-based whole-body control of such robots can generate complex dynamic behaviors through the simultaneous execution of multiple tasks. Unfortunately, tasks are generally planned without close consideration for the underlying controller being used, or the other tasks being executed, and are often infeasible when executed on the robot. Consequently, there is no guarantee that the motion will be accomplished. In this work, we develop a proof-of-concept optimization loop which automatically improves task feasibility using model-free policy search in conjunction with model-based whole-body control. This combination allows problems to be solved, which would be otherwise intractable using simply one or the other. Through experiments on both the simulated and real iCub humanoid robot, we show that by optimizing task feasibility, initially infeasible complex dynamic motions can be realized-specifically, a sit-to-stand transition. These experiments can be viewed in the accompanying Video S1.Programming by demonstration has received much attention as it offers a general framework which allows robots to efficiently acquire novel motor skills from a human teacher. While traditional imitation learning that only focuses on either Cartesian or joint space might become inappropriate in situations where both spaces are equally important (e.g., writing or striking task), hybrid imitation learning of skills in both Cartesian and joint spaces simultaneously has been studied recently. However, an important issue which often arises in dynamical or unstructured environments is overlooked, namely how can a robot avoid obstacles? In this paper, we aim to address the problem of avoiding obstacles in the context of hybrid imitation learning. Specifically, we propose to tackle three subproblems (i) designing a proper potential field so as to bypass obstacles, (ii) guaranteeing joint limits are respected when adjusting trajectories in the process of avoiding obstacles, and (iii) determining proper control commands for robots such that potential human-robot interaction is safe. By solving the aforementioned subproblems, the robot is capable of generalizing observed skills to new situations featuring obstacles in a feasible and safe manner. The effectiveness of the proposed method is validated through a toy example as well as a real transportation experiment on the iCub humanoid robot.