Learning Semantic Atomic Skills for Multi-Task Robotic Manipulation

• URL: http://arxiv.org/abs/2512.18368v1
• Date: Sat, 20 Dec 2025 13:46:08 GMT
• Title: Learning Semantic Atomic Skills for Multi-Task Robotic Manipulation
• Abstract summary: AtomSkill is a novel multi-task imitation learning framework.<n>It learns and leverages a structured Atomic Skill Space for composable robot manipulation.<n>It consistently outperforms state-of-the-art methods across diverse manipulation tasks.
• Score: 34.73582639920571
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While imitation learning has shown impressive results in single-task robot manipulation, scaling it to multi-task settings remains a fundamental challenge due to issues such as suboptimal demonstrations, trajectory noise, and behavioral multi-modality. Existing skill-based methods attempt to address this by decomposing actions into reusable abstractions, but they often rely on fixed-length segmentation or environmental priors that limit semantic consistency and cross-task generalization. In this work, we propose AtomSkill, a novel multi-task imitation learning framework that learns and leverages a structured Atomic Skill Space for composable robot manipulation. Our approach is built on two key technical contributions. First, we construct a Semantically Grounded Atomic Skill Library by partitioning demonstrations into variable-length skills using gripper-state keyframe detection and vision-language model annotation. A contrastive learning objective ensures the resulting skill embeddings are both semantically consistent and temporally coherent. Second, we propose an Action Generation module with Keypose Imagination, which jointly predicts a skill's long-horizon terminal keypose and its immediate action sequence. This enables the policy to reason about overarching motion goals and fine-grained control simultaneously, facilitating robust skill chaining. Extensive experiments in simulated and real-world environments show that AtomSkill consistently outperforms state-of-the-art methods across diverse manipulation tasks.

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