机器人 / 具身智能

Simulation is crucial in real-world robotics, offering safe, scalable, and efficient environments for developing a variety of robotic applications. While the Robot Operating System (ROS) has been widely adopted as the backbone of these robotic applications in both academia and industry, its asynchronous, multi-process design complicates reproducibility, especially across varying hardware platforms. Deterministic callback execution cannot be guaranteed when computation times and communication delays vary. This lack of reproducibility complicates scientific benchmarking and continuous integration, where consistent results are essential. To address this, we present a methodology to create deterministic simulations using ROS 2 nodes. Our ROS Simulation Library for C++ (RSLCPP) implements this approach, enabling existing nodes to be combined into a simulation routine that yields reproducible results, usually without requiring any source code changes. We demonstrate that our approach produces identical results across various CPUs and architectures when testing both a synthetic benchmark and a real-world robotics system. RSLCPP is open-sourced at https://github.com/TUMFTM/rslcpp.

Understanding the motivations underlying the human inclination to automate tasks is vital for developing robots that fit seamlessly into daily life. Accordingly, we ask: are individuals more inclined to automate activities based on the time they consume or the feelings experienced while performing them? This study explores these preferences and whether they vary across social groups, specifically gender category and income level. Leveraging data from the BEHAVIOR-1K dataset, the American Time-Use Survey, and the American Time-Use Survey Well-Being Module, we investigate the relationship between the desire for robot automation, time spent, and associated feelings: Happiness, Meaningfulness, Sadness, Painfulness, Stressfulness, or Tiredness. Our key findings show that, despite common assumptions, time spent on activities does not strongly predict automation preferences; instead, happiness and pain are the strongest indicators. We also identify differences by gender and economic level: Women prefer to automate stressful activities, whereas men prefer to automate those that make them unhappy; mid-income individuals prioritize automating less enjoyable and meaningful activities, while low and high-income show no significant correlations. We hope our research helps motivate the design of robots that align with user priorities, moving domestic robotics toward more socially relevant solutions. All data and an interactive tool are publicly available at https://robin-lab.cs.utexas.edu/why-automate-this/.