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Learning to Compose Skills

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arxiv 1711.11289 v1 pith:55LPHDZ2 submitted 2017-11-30 cs.AI

Himanshu Sahni , Saurabh Kumar , Farhan Tejani , Charles Isbell This is my paper
classification cs.AI
keywords skillscomplexcompositionfunctionlearningableallowingarchitecture
verification ladder T0 review T1 audit T2 compute T3 formal
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We present a differentiable framework capable of learning a wide variety of compositions of simple policies that we call skills. By recursively composing skills with themselves, we can create hierarchies that display complex behavior. Skill networks are trained to generate skill-state embeddings that are provided as inputs to a trainable composition function, which in turn outputs a policy for the overall task. Our experiments on an environment consisting of multiple collect and evade tasks show that this architecture is able to quickly build complex skills from simpler ones. Furthermore, the learned composition function displays some transfer to unseen combinations of skills, allowing for zero-shot generalizations.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. RoboHarness: Memory-Driven Orchestration of Heterogeneous Robot Policies for Long-Horizon Planning

    cs.RO 2026-07 reject novelty 6.0 of 10

    RoboHarness combines VLAs, RL policies, and TAMP planners via an LLM router and a memory-bridge handoff, reporting 95.2% average success on long-horizon LIBERO-LoHo versus 64.8% for the best baseline.

  2. Evolutionary Developmental Biology Can Serve as the Conceptual Foundation for a New Design Paradigm in Artificial Intelligence

    cs.AI 2025-06 conditional novelty 6.0 of 10

    The paper proposes regulatory connections, weak linkage, and component-level variation-selection, drawn from evo-devo, as the unifying conceptual foundation for a new AI design paradigm.

  3. One Rank at a Time: Cascading Error Dynamics in Sequential Learning

    cs.LG 2025-05 conditional novelty 6.0 of 10

    Errors from each rank-1 step in sequential low-rank learning compound through factors that grow when singular values are close, so early steps deserve more compute.

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