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Cognition-inspired Robot Learning and Manipulation for Assembly in Semi-structured Environments
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AI
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DOI：10.1007/s10514-026-10259-2.png)
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Abstract
En 中文
Robotic assembly in semi-structured environments represents a critical challenge in flexible manufacturing. While existing hybrid methods benefit from knowledge-based hierarchies, model-based planning, and data-driven learning, they still struggle with small-batch, precise assembly tasks due to loose coupling between components and high training costs. To address these limitations, we introduce Cognition-inspired Robot Learning and Manipulation (CRLM), a neuro-symbolic framework that tightly couples structured task knowledge, semi-supervised perception, and residual reinforcement learning for efficient skill acquisition and robust execution. Inspired by cognitive apprenticeship theory, CRLM operationalizes three developmental phases—modeling, scaffolding, and fading—through a hierarchical manipulation architecture. Specifically, explicit symbolic stage graphs and Object-Embodiment-Centric (OEC) waypoints provide interpretable task decomposition and safety bounds; semi-supervised visual learning bootstraps global and local object detectors from sparse embodied data; and a residual fine policy trained via structured-to-semi (STS) transfer learns contact-aware corrections from multimodal visual and force feedback. By integrating these components into a coherent developmental pipeline rather than treating them as isolated sequential blocks, CRLM achieves robust contact-rich assembly with minimal human engineering. Simulation experiments demonstrate substantial improvements of 13% in success rate and 15.4% reduction in completion steps compared to competing methods. Real-world experiments on peg-in-hole and gear-insertion tasks further validate the system’s effectiveness for jigless assembly in semi-structured environments.
Keywords:
Robotic assembly
Semi-structured environment
Cognition-inspired robot learning
Semi-supervised learning
Residual reinforcement learning
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