Learning from Demonstrations via Deformable Residual Multi-Attention Domain-Adaptive Meta-Learning
Year: 2025
Type: article
Abstract: In recent years, the fields of one-shot and few-shot object detection and classification have garnered significant attention. However, the rapid adaptation of robots to previously unencountered or novel environments remains a formidable challenge. Inspired by biological learning processes, meta-learning seeks to replicate the way humans and animals quickly adapt to new tasks by leveraging prior knowledge and generalizing across experiences. Despite this, traditional meta-learning methods that rely on deepening or widening neural networks offer only marginal improvements in model performance. To address this, we proposed a novel framework termed Residual Multi-Attention Domain-Adaptive Meta-Learning (DRMA-DAML). Our framework, motivated by biological principles like the human visual system's concurrent handling of global and local details for enhanced perception and decision making, empowers the model to significantly enhance performance without augmenting the depth of the neural network, thus avoiding the overfitting and vanishing gradient problems typical of deeper architectures. Empirical evidence from both simulated environments and real-world applications demonstrates that DRMA-DAML achieves state-of-the-art performance. Specifically, it improves adaptation accuracy by 11.18% on benchmark tasks and achieves a 97.64% success rate in real-world object manipulation, surpassing existing methods. These results validate the effectiveness of our approach in rapid adaptation for robotic systems.
Source: Biomimetics
Authors: Zeyu Yan, Zhongxue Gan, Gaoxiong Lu, Junxiu Liu, Wei Li
Institutions: Fudan University, Guangxi Normal University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,654
OpenAlex ID: W4407353355