ViterbiPlanNet: Injecting Procedural Knowledge via Differentiable Viterbi for Planning in Instructional Videos

• URL: http://arxiv.org/abs/2603.04265v1
• Date: Wed, 04 Mar 2026 16:50:07 GMT
• Title: ViterbiPlanNet: Injecting Procedural Knowledge via Differentiable Viterbi for Planning in Instructional Videos
• Authors: Luigi Seminara, Davide Moltisanti, Antonino Furnari,
• Abstract summary: Procedural planning aims to predict a sequence of actions that transforms an initial visual state into a desired goal.<n>Existing approaches rely on large-scale models that learn procedural structures implicitly.<n>We introduce ViterbiPlanNet, a principled framework that explicitly integrates procedural knowledge into the learning process.
• Score: 15.697653554425045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Procedural planning aims to predict a sequence of actions that transforms an initial visual state into a desired goal, a fundamental ability for intelligent agents operating in complex environments. Existing approaches typically rely on large-scale models that learn procedural structures implicitly, resulting in limited sample-efficiency and high computational cost. In this work we introduce ViterbiPlanNet, a principled framework that explicitly integrates procedural knowledge into the learning process through a Differentiable Viterbi Layer (DVL). The DVL embeds a Procedural Knowledge Graph (PKG) directly with the Viterbi decoding algorithm, replacing non-differentiable operations with smooth relaxations that enable end-to-end optimization. This design allows the model to learn through graph-based decoding. Experiments on CrossTask, COIN, and NIV demonstrate that ViterbiPlanNet achieves state-of-the-art performance with an order of magnitude fewer parameters than diffusion- and LLM-based planners. Extensive ablations show that performance gains arise from our differentiable structure-aware training rather than post-hoc refinement, resulting in improved sample efficiency and robustness to shorter unseen horizons. We also address testing inconsistencies establishing a unified testing protocol with consistent splits and evaluation metrics. With this new protocol, we run experiments multiple times and report results using bootstrapping to assess statistical significance.

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