QBIT: Quality-Aware Cloud-Based Benchmarking for Robotic Insertion Tasks

2025-03-10

AI Summary

Robotic insertion tasks, characterized by strong environmental interactions, cannot be adequately evaluated by success rate alone for industrial-grade quality and reliability assurance. This paper proposes the first quality-aware cloud benchmarking framework, transcending the limitation of single-metric evaluation by incorporating multidimensional quality metrics—including energy efficiency, force smoothness, and task completion time—and enabling joint simulation-to-real evaluation. We introduce a novel benchmarking paradigm that unifies quality and reliability assessment; develop contact parameter randomization and perception uncertainty modeling techniques, reducing the sim-to-real gap—improving contact simulation accuracy by 32%; and design a microservice-based ROS 2 container architecture deployed via Kubernetes and Docker for cross-platform reproducibility and scalable deployment. Systematic evaluation is conducted across MuJoCo simulation and real robotic platforms on geometric, force-controlled, and learning-based insertion algorithms, accelerating the transfer of laboratory innovations to industrial applications.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

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: geometricbased, 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 GitHub.

Problem

• Develops QBIT for quality-aware benchmarking of robotic insertion tasks.
• Addresses sim-to-real gap with randomized parameters and large-scale experiments.
• Evaluates insertion approaches using simulation and real-world testing.

Innovation

• Quality-aware benchmarking with additional metrics
• Randomized contact parameters in MuJoCo simulator
• ROS2 with containerization for seamless transitions