Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

Source note3 min read

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

Source

arXiv

arxiv.org/abs/2607.02466v1

Read the source

Source

Plain-Language Brief

Researchers propose a new framework called Task-Agnostic Pretraining (TAP) for Vision-Language-Action (VLA) models. The TAP aims to address the scarcity of expert demonstrations by separating two learning objectives: acquiring physical competence and acquiring semantic alignment. The first stage learns transferable motor priors from cheap, unlabeled interaction data using a self-supervised Inverse Dynamics objective. The second stage uses minimal expert data to ground these priors in language. This method reduces the need for large amounts of labeled data and outperforms standard behavior cloning on the SIMPLER benchmark.

What the Document Says

  • The paper presents a two-stage framework called Task-Agnostic Pretraining (TAP) for Vision-Language-Action (VLA) models.
  • The TAP aims to address the scarcity of expert demonstrations by separating learning objectives: physical competence and semantic alignment.
  • The first stage learns transferable motor priors from cheap, unlabeled interaction data using a self-supervised Inverse Dynamics objective.
  • The second stage uses minimal expert data to ground these priors in language.
  • On the SIMPLER benchmark, TAP matches models trained on over 1M expert trajectories while using orders of magnitude less labeled data.
  • TAP outperforms standard behavior cloning by a 10% absolute gain.

Key Concepts Mentioned

  • Vision-Language-Action (VLA) models
  • Task-Agnostic Pretraining (TAP)
  • self-supervised Inverse Dynamics objective
  • SIMPLER benchmark
  • behavior cloning

Claims Made by the Document

  • Scarcity of expert demonstrations is a bottleneck in VLA models.
  • The bottleneck stems from conflating two learning objectives: physical competence and semantic alignment.
  • Only the latter requires language supervision.
  • TAP can match models trained on over 1M expert trajectories with less labeled data.
  • TAP outperforms standard behavior cloning.

Evidence Used

  • The text states that VLA models are bottlenecked by the scarcity of expert demonstrations.
  • The paper argues that this bottleneck stems from conflating two learning objectives: acquiring physical competence and acquiring semantic alignment.
  • The authors claim that only the latter requires language supervision.

Methods / Approach

  • Two-stage framework: self-supervised Inverse Dynamics objective for learning motor priors, lightweight second stage to ground these 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 state any limitations from the agent's perspective.

Open Questions

  • What is the exact nature of the self-supervised Inverse Dynamics objective?
  • How does the second stage ground motor priors in language?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper provides a detailed explanation of the proposed framework but lacks information on the specifics of the self-supervised Inverse Dynamics objective and the method used to ground motor priors in language.

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.