Insertion tasks are fundamental yet challenging for robots, particularly in autonomous operations, due to their continuous interaction with the environment. AI-based approaches appear to be up to the challenge, but in production they must not only achieve high success rates. They must also ensure insertion quality and reliability. To address this, we introduce QBIT, a quality-aware benchmarking framework that incorporates additional metrics such as force energy, force smoothness and completion time to provide a comprehensive assessment. To ensure statistical significance and minimize the sim-to-real gap, we randomize contact parameters in the MuJoCo simulator, account for perceptual uncertainty, and conduct large-scale experiments on a Kubernetes-based infrastructure. Our microservice-oriented architecture ensures extensibility, broad applicability, and improved reproducibility. To facilitate seamless transitions to physical robotic testing, we use ROS2 with containerization to reduce integration barriers. We evaluate QBIT using three insertion approaches: geometric-based, force-based, and learning-based, in both simulated and real-world environments. In simulation, we compare the accuracy of contact simulation using different mesh decomposition techniques. Our results demonstrate the effectiveness of QBIT in comparing different insertion approaches and accelerating the transition from laboratory to real-world applications. Code is available on GitHub3.

This paper presents ETA-IK, a novel Execution-Time-Aware Inverse Kinematics method tailored for dual-arm robotic systems. The primary goal is to optimize motion execution time by leveraging the redundancy of the entire system, specifically in tasks where only the relative pose of the robots is constrained, such as dual-arm scanning of unknown objects. Unlike traditional IK methods using surrogate metrics, our approach directly optimizes execution time while implicitly considering collisions. A neural network based execution time approximator is employed to predict time-efficient joint configurations while accounting for potential collisions. Through experimental evaluation on a system composed of a UR5 and a KUKA iiwa robot, we demonstrate significant reductions in execution time. The proposed method outperforms conventional approaches, showing improved motion efficiency without sacrificing positioning accuracy.

Offloading software modules to the edge/cloud can enhance a robot’s capabilities by leveraging massive computing power. However, determining which software module should be offloaded and scheduled to which robot/edge/cloud node is a challenging task, particularly for robot fleets with diverse tasks. In this paper, we tackle the software scheduling problem and introduce a taxonomy to categorize software modules and classify their applicability and requirements for offloading. Additionally, by using prior measurements, we model the compute cluster and formalize software scheduling as a multi-objective optimization problem which we tackle with a genetic algorithm. To evaluate our approach with a challenging setup, we build a mobile manipulation task using open-source frameworks and libraries in the Robot Operating System (ROS2) community in simulation as well as a mildly simplified real-world variant. Our evaluation shows significant improvements compared to the built-in scheduler of Kubernetes (K8s) regarding robotic specific metrics such as the rate of missed cycle time in both simulated and real-world experiments.

As advanced algorithms enable robots to handle more challenging tasks and operate more autonomously, the on-board computer cannot meet the increased demands regarding computing power and memory storage in an efficient way. Leveraging the massive computing power of the cloud and low-latency connectivity to the edge can compensate for this lack of computing resources. However, this introduces a new challenge related to the deployment of complex robotic software across multiple devices, especially in a large-scale system. This paper presents KubeROS, a unified and fully managed platform for automated deployment of robotic applications developed on top of Robot Operating System 2 (ROS2), in a hybrid computing infrastructure with robots, edge and cloud. KubeROS uses Kubernetes from Cloud Native Computing as its underlying software orchestration framework. It aims to help researchers and developers with no prior cloud computing knowledge deploy their ROS2-based robotic applications at any scale. KubeROS eliminates the need for system configuration and network setup. We demonstrate the applicability of KubeROS by deploying a fleet of simulated mobile manipulators in a clas-sical pick-and-place application. The experiments demonstrate the effects of different deployment strategies for vision-based motion planning under different fleet sizes and workloads. In addition, KubeROS improves task performance by using high-performance computing at the edge and in the cloud, and achieves high resource efficiency when using the shared deployment strategy.