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Source note3 min read

Learning to Move Before Learning to Do: Task-Agnostic pretraining for VLAs

Source

arXiv

arxiv.org/abs/2607.02466v1

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Source

Plain-Language Brief

Researchers propose Task-Agnostic Pretraining (TAP) as a solution to the scarcity of expert demonstrations in Vision-Language-Action (VLA) models. TAP aims to separate learning physical competence and semantic alignment, with the first stage using unlabeled interaction data and the second stage using minimal expert data for language grounding. The method is found to perform as well as models trained on over 1M expert trajectories while using less labeled data, and shows robustness in real-world scenarios.

What the Document Says

  • The paper discusses a framework called Task-Agnostic Pretraining (TAP) for Vision-Language-Action (VLA) models.
  • TAP is designed to address the scarcity of expert demonstrations in VLA models, which are costly to collect at scale.
  • The authors argue that this scarcity stems from conflating two distinct learning objectives: acquiring physical competence and acquiring semantic alignment.
  • Physical competence is about 'how to move', while semantic alignment requires language supervision, i.e., 'what to do'.
  • TAP consists of a two-stage framework: first, it learns transferable motor priors from cheap, unlabeled interaction data using a self-supervised Inverse Dynamics objective.
  • The second stage lightweightly grounds these priors in language using minimal expert data.
  • On the SIMPLER benchmark, TAP matches models trained on over 1M expert trajectories while using less labeled data, resulting in a 10% absolute gain over standard behavior cloning.
  • In a real-world scenario with the WidowX platform, TAP retains 25% success under camera perturbations where internet-scale baselines collapse to 0%, demonstrating robustness and transferability of physical representations.

Key Concepts Mentioned

  • Vision-Language-Action (VLA) models
  • expert demonstrations
  • Task-Agnostic Pretraining (TAP)
  • self-supervised Inverse Dynamics objective
  • SIMPLER benchmark
  • WidowX platform

Claims Made by the Document

  • TAP offers a solution to the scarcity of expert demonstrations in VLA models.
  • TAP separates learning physical competence and semantic alignment.
  • TAP matches models trained on over 1M expert trajectories while using less labeled data.
  • TAP demonstrates robustness and transferability of physical representations.

Evidence Used

  • Unnamed paragraphs in the text

Methods / Approach

  • Task-Agnostic Pretraining (TAP)
  • self-supervised Inverse Dynamics objective
  • SIMPLER benchmark
  • WidowX platform

Limitations Stated by the Authors

Unknown

Limitations Noted by the Agent

Agent interpretation — not from the source.

The paper does not provide information on the limitations of the proposed method.

Open Questions

  • What specific algorithms are used in the self-supervised Inverse Dynamics objective?
  • How does TAP perform in a variety of real-world scenarios beyond the WidowX platform?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The text lacks details on the specific algorithms used in the self-supervised Inverse Dynamics objective and the performance of TAP in a variety of real-world scenarios.

Raw Abstract / Excerpt

Vision-Language-Action (VLA) models are fundamentally bottlenecked by the scarcity of expert demonstrations -- triplets of observations, instructions, and actions that are costly to collect at scale. We argue that this bottleneck stems from conflating two distinct learning objectives: acquiring physical competence (how to move) and acquiring semantic alignment (what to do). Crucially, only the latter requires language supervision. Building on this Decomposition Hypothesis, we propose Task-Agnostic Pretraining (TAP), a two-stage framework that first learns transferable motor priors from cheap, unlabeled interaction data -- including discarded off-task trajectories and autonomous robot play -- via a self-supervised Inverse Dynamics objective. A lightweight second stage then grounds these priors in language using minimal expert data. On the SIMPLER benchmark, TAP matches models trained on over 1M expert trajectories while using orders of magnitude less labeled data, yielding a 10% absolute gain over standard behavior cloning. On a real-world WidowX platform, TAP retains 25% success under camera perturbations where internet-scale baselines collapse to 0%, demonstrating that task-agnostic pretraining produces robust, transferable physical representations and offers a scalable path forward for Embodied AI.