Dynamics-Aware Meta-Imitation for Generalization to Unseen Robotic Manipulation
Dynamics-Aware Meta-Imitation for Generalization to Unseen Robotic Manipulation
This research introduces the DAMI framework to improve generalization in robotic manipulation tasks. By combining meta-learning with a Visual-Motor Trajectory module and an Unpaired Unified Task block, the system captures complex spatio-temporal dynamics and fuses multimodal observations. The approach enables rapid adaptation to novel tasks through few-shot fine-tuning, outperforming state-of-the-art baselines in both simulated and real-world environments.
The paper proposes the DAMI framework to address generalization gaps in imitation learning by integrating meta-learning and a Visual-Motor Trajectory module, with results demonstrated in both simulation and real-world settings.
Source: arXiv Robotics — research abstracts · Read original article ↗
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arXiv:2607.15880v2 Announce Type: replace Abstract: Imitation Learning aims to learn skills from extensive observations and demonstrations for robots, so it suffers from data scarcity and environment generalization. The existing methods predominantly focus on imitation from in-domain tasks and consequently struggle with generalization to unseen tasks. To bridge this generalization gap, we propose the \textbf{D}ynamic
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s-\textbf{A}ware \textbf{M}eta-\textbf{I}mitation (DAMI) framework. By integrating meta-learning to construct a shared skill space, DAMI equips agents for rapid adaptation to novel tasks. We introduce the Visual-Motor Trajectory (VMT) module to capture complex spatio-temporal dynamics within the task latent space. Furthermore, we propose the Unpaired Unified Task (U2T) block to fuse unstructured multimodal observatio
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ns. To coordinate these representations, we integrate a Task-Conditioned Feature Modulation (TCFM) mechanism customized for modulating low-level 3D features. By capturing intrinsic dynamics from a random complete reference demonstration, our framework learns the underlying task logic rather than memorizing static cues, ensuring effective generalization. Extensive experiments in both simulation and real-world settings
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demonstrate that our approach outperforms state-of-the-art baselines regarding direct inference on seen tasks and adaptation to unseen tasks via few-shot fine-tuning.
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Dynamics-Aware Meta-Imitation for Generalization to Unseen Robotic Manipulation
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Source：arXiv Robotics — research abstracts · arxiv.org