Optimizing Modular Robot Composition: A Lexicographic Genetic Algorithm Approach

Industrial robots are designed as general-purpose hardware, which limits their ability to adapt to changing task requirements or environments. Modular robots, on the other hand, offer flexibility and can be easily customized to suit diverse needs. The morphology, i.e., the form and structure of a robot, significantly impacts the primary performance metrics acquisition cost, cycle time, and energy efficiency. However, identifying an optimal module composition for a specific task remains an open problem, presenting a substantial hurdle in developing task-tailored modular robots. Previous approaches either lack adequate exploration of the design space or the possibility to adapt to complex tasks. We propose combining a genetic algorithm with a lexicographic evaluation of solution candidates to overcome this problem and navigate search spaces exceeding those in prior work by magnitudes in the number of possible compositions. We demonstrate that our approach outperforms a state-of-the-art baseline and is able to synthesize modular robots for industrial tasks in cluttered environments.

Timor Python: A Toolbox for Industrial Modular Robotics

Development of controllers, novel robot kinematics, and learning-based applications of robotics today happens almost exclusively in simulation first before being implemented in the real world. In particular, Modular Reconfigurable Robots (MRRs) are an exciting innovation in industrial robotics, promising greater flexibility, improved maintainability, and cost-efficiency compared to traditional manipulators. However, there is no tool or standardized way to simulate and model assemblies of modules in the same way it has been done for robotic manipulators for decades. We introduce the Toolbox for Industrial Modular Robotics (Timor), a python toolbox to bridge this gap and integrate modular robotics in existing simulation and optimization pipelines. Our open-source library comes with various examples as well as tutorials and can easily be integrated with existing simulation tools - not least by offering URDF export of arbitrary modular robot assemblies, enabling rapid model generation.

United States Politicians' Tone Became More Negative with 2016 Primary Campaigns

There is a widespread belief that the tone of US political language has become more negative recently, in particular when Donald Trump entered politics. At the same time, there is disagreement as to whether Trump changed or merely continued previous trends. To date, data-driven evidence regarding these questions is scarce, partly due to the difficulty of obtaining a comprehensive, longitudinal record of politicians' utterances. Here we apply psycholinguistic tools to a novel, comprehensive corpus of 24 million quotes from online news attributed to 18,627 US politicians in order to analyze how the tone of US politicians' language evolved between 2008 and 2020. We show that, whereas the frequency of negative emotion words had decreased continuously during Obama's tenure, it suddenly and lastingly increased with the 2016 primary campaigns, by 1.6 pre-campaign standard deviations, or 8% of the pre-campaign mean, in a pattern that emerges across parties. The effect size drops by 40% when omitting Trump's quotes, and by 50% when averaging over speakers rather than quotes, implying that prominent speakers, and Trump in particular, have disproportionately, though not exclusively, contributed to the rise in negative language. This work provides the first large-scale data-driven evidence of a drastic shift toward a more negative political tone following Trump's campaign start as a catalyst, with important implications for the debate about the state of US politics.

cRoK: A Composable Robotics Benchmark

Selecting an optimal robot and configuring it for a given task is currently mostly done by human expertise or trial and error. To evaluate automatic selection and adaptation of robots to specific tasks, we introduce a benchmark suite encompassing a common format for robots, environments, and task descriptions. Our benchmark suite is especially useful for modular robots, where the creation of the robots themselves creates a host of additional parameters to optimize. The benchmark defines this optimization and facilitates the comparison of solution algorithms. All benchmarks are accessible through a website to conveniently share, reference, and compare solutions.