# Learning from Demonstrations via Deformable Residual Multi-Attention Domain-Adaptive Meta-Learning

## TL;DR

This paper introduces a new meta-learning framework that improves robot adaptation to new environments without deepening neural networks.

## Contribution

Proposes DRMA-DAML, a domain-adaptive meta-learning method inspired by human visual processing for rapid robot adaptation.

## Key findings

- DRMA-DAML improves adaptation accuracy by 11.18% on benchmark tasks.
- Achieves a 97.64% success rate in real-world object manipulation.
- Avoids overfitting and vanishing gradients without increasing network depth.

## 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.