Robot Continual Learning With Knowledge Sharing and Model Acceleration for Sequential Assembly Tasks

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Abstract
Within the domain of robot assembly manipulation, contemporary skill-learning approaches exhibit significant generalization capabilities, enabling them to adapt to a broad spectrum of tasks and dynamic environmental conditions. However, these methods suffer from catastrophic forgetting when applied to various robot manipulation tasks, leading to suboptimal performance and inadequate adaptability in real-world scenarios. To this end, a continual learning strategy that incorporates both knowledge sharing and model acceleration is proposed for multiple robot assembly tasks. First, a common experience pool is established that enhances data efficiency by selectively revisiting past task experiences during the learning process. This mechanism mitigates forgetting by preserving critical knowledge from previous tasks. Second, incorporating the effects of the old model, the new model can converge more rapidly. The weights of the old and new models are learned through interaction with the environment. Experiments on various assembly tasks are conducted to verify the continuous memory retention and efficient learning capabilities of the proposed method in both simulation and real-world environments.
Funding Information
  • National Key Research and Development Plan of China (2023YFB4705002)
  • China Postdoctoral Science Foundation (2025M781652)
  • General program of the National Natural Science Foundation of China (62373225)

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