Meta-Learning for Few-Shot Adaptation in Robotic Control Tasks

Meta-learning is emerging as a powerful paradigm to allow robots to learn new tasks quickly and with little data, to address an obstacle in robot control. This paper investigates few-shot adaptation methods such as MAML, Reptile, and prototypical network that leverage experience to promote performance on a wide range of manipulation, locomotion, and grasping tasks. By observing a collection (distribution) of simulated or real-world tasks, meta-learning algorithms adapt initialization parameters or latent features such that fast fine-tuning (typically across 5-10 trials) is possible for new tasks. Key contributions include memory-augmented neural networks for contextual memory, gradient-based optimization for fast parameter updates, and hierarchical meta-policies for exploiting multi-task scale. Get this Paper Challenges remain in the sim-to-real transfer setting, in which dynamics mismatches and sensory noise lead to poor performance; the paper further investigates domain randomization and latent space alignment. We measure metrics such as adaptation time, task success rate, and sample efficiency over benchmark environments (e.g., Meta-World, RoboSuite). It is found that meta-learning reduces the requirement for data by 70-80% compared to traditional reinforcement learning and achieves robustness against kinematic variations and environmental distractors. Future work may consider fusing physics-informed priors and the hybrid meta-learning architecture, containing meta-learning and imitation learning. This work highlights the promise of meta-learning in enabling democratized robotic deployment across unstructured settings from warehouses to homes, where task diversity and data scarcity are becoming central limitations.

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