Related papers: Synchronizing Machine Learning Algorithms, Realtim…
Accurate and safe robotic manipulation under dynamic and visually occluded conditions remains a core challenge in real-world deployment. We introduce SyncTwin, a novel digital twin framework that unifies fast 3D scene reconstruction and…
Robotics programming typically involves a trade-off between the ease of use offered by Python and the run-time performance of C++. While multi-language architectures address this trade-off by coupling Python's ergonomics with C++'s speed,…
Human-robot interactions have been recognized to be a key element of future industrial collaborative robots (co-robots). Unlike traditional robots that work in structured and deterministic environments, co-robots need to operate in highly…
Testing and debugging have become major obstacles for robot software development, because of high system complexity and dynamic environments. Standard, middleware-based data recording does not provide sufficient information on internal…
The underlying framework for controlling autonomous robots and complex automation applications are Operating Systems (OS) capable of scheduling perception-and-control tasks, as well as providing real-time data communication to other robotic…
The control of manufacturing processes must satisfy high quality and efficiency requirements while meeting safety requirements. A broad spectrum of monitoring and control strategies, such as model- and optimization-based controllers, are…
ROS (Robot Operating System) has become ubiquitous for testing new algorithms, alternative hardware configurations, and prototyping. By performing research with its modular framework, it can streamline sharing new work and integrations.…
Systems based on the Robot Operating System (ROS) are easy to extend with new on-line algorithms and devices. However, there is relatively little support for coordinating a large number of heterogeneous sub-systems. In this paper we propose…
Robotics applications process large amounts of data in real-time and require compute platforms that provide high performance and energy-efficiency. FPGAs are well-suited for many of these applications, but there is a reluctance in the…
Simulation is used extensively in autonomous systems, particularly in robotic manipulation. By far, the most common approach is to train a controller in simulation, and then use it as an initial starting point for the real system. We…
This paper introduces RobotIQ, a framework that empowers mobile robots with human-level planning capabilities, enabling seamless communication via natural language instructions through any Large Language Model. The proposed framework is…
The growing ambition for space exploration demands robust autonomous systems that can operate in unstructured environments under extreme extraterrestrial conditions. The adoption of robot learning in this domain is severely hindered by the…
In reinforcement learning (RL) research, simulations enable benchmarks between algorithms, as well as prototyping and hyper-parameter tuning of agents. In order to promote RL both in research and real-world applications, frameworks are…
The development of autonomous aerial systems, particularly for multi-robot configurations, is a complex challenge requiring multidisciplinary expertise. Unlike ground robotics, aerial robotics has seen limited standardization, leading to…
Automated driving is currently a prominent area of scientific work. In the future, highly automated driving and new Advanced Driver Assistance Systems will become reality. While Advanced Driver Assistance Systems and automated driving…
As the robotics systems increasingly integrate into daily life, from smart home assistants to the new-wave of industrial automation systems (Industry 4.0), there's an increasing need to bridge the gap between complex robotic systems and…
The paper presents a software architecture to optimize the process of prototyping and deploying robot controllers that are synthesized using model-based design methodologies. The architecture is composed of a framework and a pipeline.…
Intelligent mobile robots are critical in several scenarios. However, as their computational resources are limited, mobile robots struggle to handle several tasks concurrently and yet guaranteeing real-timeliness. To address this challenge…
One key technical challenge in the age of autonomous machines is the programming of autonomous machines, which demands the synergy across multiple domains, including fundamental computer science, computer architecture, and robotics, and…
Collaborative robots are becoming part of intelligent automation systems in modern industry. Development and control of such systems differs from traditional automation methods and consequently leads to new challenges. Thankfully, Robot…