Evaluating Model-Agnostic Meta-Learning on MetaWorld ML10 Benchmark: Fast Adaptation in Robotic Manipulation Tasks
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911268105879552 |
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| author | Atamuradov, Sanjar |
| author_facet | Atamuradov, Sanjar |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_12383 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating Model-Agnostic Meta-Learning on MetaWorld ML10 Benchmark: Fast Adaptation in Robotic Manipulation Tasks Atamuradov, Sanjar Robotics 68T05, 68T07 I.2.9; I.2.6 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. |
| title | Evaluating Model-Agnostic Meta-Learning on MetaWorld ML10 Benchmark: Fast Adaptation in Robotic Manipulation Tasks |
| topic | Robotics 68T05, 68T07 I.2.9; I.2.6 |
| url | https://arxiv.org/abs/2511.12383 |