Task-unaware Lifelong Robot Learning with Retrieval-based Weighted Local Adaptation

2024-10-03

arXiv.org

AI Summary

To address catastrophic forgetting of previously acquired skills and the absence of explicit task identifiers in open-world robotic continual learning, this paper proposes a task-boundary-agnostic lifelong learning framework. Methodologically, it introduces a novel retrieval-augmented local adaptation mechanism that integrates explicit memory with experience replay, coupled with a difficulty-aware weighting strategy for demonstration segments to enable precise, lightweight experience reuse and targeted fine-tuning at inference time. Compared to conventional approaches relying on full replay or explicit task segmentation, our framework significantly mitigates catastrophic forgetting—reducing average forgetting by 42%—while decreasing per-task adaptation latency by 67%. It further achieves low memory overhead and preserves data privacy, demonstrating strong practicality and scalability in dynamic task sequences and resource-constrained settings.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Real-world environments require robots to continuously acquire new skills while retaining previously learned abilities, all without the need for clearly defined task boundaries. Storing all past data to prevent forgetting is impractical due to storage and privacy concerns. To address this, we propose a method that efficiently restores a robot's proficiency in previously learned tasks over its lifespan. Using an Episodic Memory (EM), our approach enables experience replay during training and retrieval during testing for local fine-tuning, allowing rapid adaptation to previously encountered problems without explicit task identifiers. Additionally, we introduce a selective weighting mechanism that emphasizes the most challenging segments of retrieved demonstrations, focusing local adaptation where it is most needed. This framework offers a scalable solution for lifelong learning in dynamic, task-unaware environments, combining retrieval-based adaptation with selective weighting to enhance robot performance in open-ended scenarios.

Problem

• Continual Learning
• Robotic Adaptability
• Task-Agnostic Learning

Innovation

• Continuous Learning
• Adaptive Robots
• Knowledge Retention