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Source note2 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), a two-stage framework to train Vision-Language-Action (VLA) models. TAP first learns transferable motor priors from unlabeled interaction data using self-supervised Inverse Dynamics, then grounds these priors in language with minimal expert data. The method requires less labeled data and outperforms standard behavior cloning on the SIMPLER benchmark.

What the Document Says

  • The researchers propose a two-stage framework called Task-Agnostic Pretraining (TAP).
  • TAP learns transferable motor priors from unlabeled interaction data first, then grounds these priors in language using minimal expert data.
  • The goal is to train Vision-Language-Action (VLA) models.
  • Scarcity of expert demonstrations (triplets of observations, instructions, and actions) is a bottleneck in VLA models due to their costly collection at scale.
  • The authors argue that this bottleneck arises from conflating two distinct learning objectives: acquiring physical competence and acquiring semantic alignment.

Key Concepts Mentioned

  • Vision-Language-Action (VLA) models
  • Expert demonstrations
  • Task-Agnostic Pretraining (TAP)
  • Motor priors
  • Self-supervised Inverse Dynamics
  • SIMPLER benchmark

Claims Made by the Document

  • TAP matches models trained on over 1M expert trajectories while using orders of magnitude less labeled data.
  • TAP offers a scalable path forward for Embodied AI.

Evidence Used

  • On the SIMPLER benchmark, TAP shows improved performance over standard behavior cloning.

Methods / Approach

  • Self-supervised Inverse Dynamics
  • Two-stage framework: learning transferable motor priors followed by grounding in language with minimal expert data.

Limitations Stated by the Authors

  • Not stated in source

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The effectiveness of TAP on different types of tasks or platforms is not evaluated in this paper.

Open Questions

  • How does TAP perform on various real-world VLA tasks and platforms?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The paper focuses on the SIMPLER benchmark for evaluating the performance of the proposed method.
  • The authors claim that TAP can retain success under camera perturbations, but no specific numbers are provided.

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.