Efficiently Linking Real Scenes with Synthetic Data Generation for AI-based Cognitive Robotics and Computer Vision Applications

2026-06-18

AI Summary

Current AI vision models face significant limitations in cognitive robotics applications due to scarce training data, substantial domain gaps between simulation and reality, and insufficient generalization capabilities, hindering their ability to meet the demands for accuracy and scalability in industrial and domestic settings. This work proposes a synergistic training paradigm that integrates real and synthetic data, jointly optimizing data generation and model architecture to establish a domain-adaptive framework encompassing semantic understanding, 6D pose estimation, and grasp pose prediction. By effectively bridging the sim-to-real domain gap, the proposed approach substantially enhances cross-domain generalization, perceptual accuracy, and task success rates, thereby overcoming the performance bottlenecks inherent in conventional transfer learning methods.

Abstract

AI vision models are a driving factor for the potential use case scenarios of cognitive robotics within in the industry and household applications. A large array of methods from semantic environment analysis towards 6D and grasping pose estimation have been proposed based on the latest AI achievements. However, such advancements require further strong and efficient methods w.r.t. training data and AI-architectures, which are capable in synergy to tackle current challenges, precision limits, and scalability beyond domain gaps. In this paper, we discuss these current limits and trends in the related state-of-the-art which are challenging those. Further we discuss our current work in progress on bridging the domain gap between simulations and real world applications by linking those in the training data generation.

Problem

• domain gap
• synthetic data
• real-world applications
• AI vision models
• cognitive robotics

Innovation

• domain gap
• synthetic data generation
• cognitive robotics
• AI vision models
• real-to-sim linking