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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

The authors propose a new approach called Task-Agnostic Pretraining (TAP) for Vision-Language-Action (VLA) models to overcome the scarcity of expert demonstrations. TAP aims to separate learning physical competence and semantic alignment, which requires language supervision. It first learns transferable motor priors from cheap, unlabeled interaction data via a self-supervised Inverse Dynamics objective. Then, it uses minimal expert data for grounding these priors in language. The authors claim that TAP performs as well as models trained on over 1 million expert trajectories while using much less labeled data, and it demonstrates robust performance on a real-world platform.

What the Document Says

  • The paper discusses Task-Agnostic Pretraining (TAP) for Vision-Language-Action (VLA) models to overcome the scarcity of expert demonstrations. TAP separates learning physical competence and semantic alignment. It uses a self-supervised Inverse Dynamics objective to learn transferable motor priors from cheap, unlabeled interaction data and minimal expert data for language grounding.
  • The SIMPLER benchmark shows that TAP matches models trained on over 1M expert trajectories while using less labeled data, with a 10% absolute gain over standard behavior cloning.
  • On the WidowX platform, TAP retains success under camera perturbations where internet-scale baselines collapse to 0%.

Key Concepts Mentioned

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

Claims Made by the Document

  • TAP matches or outperforms models trained on large amounts of labeled data.
  • TAP produces robust, transferable physical representations.

Evidence Used

  • Results from the SIMPLER benchmark and the WidowX platform.

Methods / Approach

  • Task-Agnostic Pretraining (TAP) with self-supervised Inverse Dynamics objective for motor priors learning.
  • Grounding motor priors in language using minimal expert data.

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The paper does not discuss the limitations of TAP in detail. Potential limitations could include generalization to various tasks and environments, or the quality of the unlabeled interaction data.

Open Questions

  • How well does TAP generalize to various tasks and environments?
  • What impact does the quality of the unlabeled interaction data have on TAP's performance?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper does not provide a detailed comparison between TAP and other methods on multiple tasks or environments. Also, it does not elaborate on how the quality of unlabeled interaction data affects TAP's performance.

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.