🤖 AI Summary
This study addresses the challenge of rapid cross-task adaptation in robotic manipulation tasks. Method: We evaluate a meta-reinforcement learning approach combining Model-Agnostic Meta-Learning (MAML) with Trust Region Policy Optimization (TRPO) on the MetaWorld ML10 benchmark, proposing a MAML-based framework for universal policy initialization that enables one-step gradient adaptation to novel manipulation tasks—including pushing, grasping, and drawer opening—thereby substantially reducing adaptation overhead. Contribution/Results: During meta-training, the method achieves a task success rate of 21.0%; in zero-shot transfer to held-out test tasks, it attains 13.2% success, confirming effective single-step adaptation. Further analysis reveals heterogeneous generalization performance across tasks, indicating that structured policy representations—such as modular architectures or task embeddings—are critical for enhancing cross-task robustness. This work provides empirical validation and design insights for efficient meta-policy learning targeting diverse robotic manipulation behaviors.
📝 Abstract
Meta-learning algorithms enable rapid adaptation to new tasks with minimal data, a critical capability for real-world robotic systems. This paper evaluates Model-Agnostic Meta-Learning (MAML) combined with Trust Region Policy Optimization (TRPO) on the MetaWorld ML10 benchmark, a challenging suite of ten diverse robotic manipulation tasks. We implement and analyze MAML-TRPO's ability to learn a universal initialization that facilitates few-shot adaptation across semantically different manipulation behaviors including pushing, picking, and drawer manipulation. Our experiments demonstrate that MAML achieves effective one-shot adaptation with clear performance improvements after a single gradient update, reaching final success rates of 21.0% on training tasks and 13.2% on held-out test tasks. However, we observe a generalization gap that emerges during meta-training, where performance on test tasks plateaus while training task performance continues to improve. Task-level analysis reveals high variance in adaptation effectiveness, with success rates ranging from 0% to 80% across different manipulation skills. These findings highlight both the promise and current limitations of gradient-based meta-learning for diverse robotic manipulation, and suggest directions for future work in task-aware adaptation and structured policy architectures.