τ_0-VLA長程操控

Papers arxiv:2608.

16885 Copy markdown τ0-VLA: a Hierarchical Robot Foundation Model with World-Model-Guided Test-Time Computation Published on Aug 17 · Submitted by jrryzh(SII) on Aug 21 · Shanghai Innovation Institute Upvote 14 +6 Authors: Xiaowei Cai ,Yunuo Cai ,Bingao Chen ,Jingxiao Chen ,Zhi Chen ,Siyuan Feng ,Tengyu Hou ,Jingshun Huang ,Han Jiang ,Runkun Ju ,Dong Li ,Mingxiang Li ,Shaowei Li ,Xinchen Li ,Yifan Li ,Yi Liu ,Zhongyuan Liu ,Jianlan Luo ,Junwen Miao ,Ruiqi Ni ,Buqing Nie ,Mingjie Pan +17 authors Abstract A hierarchical vision-language-action model improves long-horizon robot manipulation by using world-model-guided test-time search to scale computation for high-level subtask decisions.

Generated by thinkingmachines/Inkling-Small Long-horizon robot manipulation requires a robot to both execute individual skills reliably and sequence them coherently over extended tasks.

Most hierarchical vision-language-action (VLA) models make each such decision with a single forward pass, leaving no mechanism to allocate additional computation to difficult or consequential choices.

We introduce τ0-VLA, a hierarchical robot foundation model that formulates high-level subtask generation as a compute-scalable inference problem through world-model-guided test-time computation.

At each inference step, the high-level policy uses execution memory to generate a subtask and, when needed, searches over alternatives before committing to its output.A low-level policy then executes the generated subtask across multiple robot embodiments.

The policy is trained on 40,115 hours of heterogeneous real-world data with multimodal co-training.

Across in-domain and distribution-shifted settings, allocating additional test-time computation substantially improves next-subtask prediction accuracy, and these gains translate into higher closed-loop success on long-horizon robot manipulation tasks.

View arXiv page View PDF Project page GitHub 528 Add to collection Community J3rr1 Paper author Paper submitter 2 days ago 🤖 What if a robot could compare possible futures before deciding what to do next?We introduce τ₀-VLA, a hierarchical robot foundation model for long-horizon manipulation.

Its high-level policy maintains execution memory and, when a decision is uncertain, allocates additional test-time computation to propose candidate subtasks, predict their visual consequences with a world model, and compare alternatives before committing.

A generalist low-level VLA then executes the selected subtask across robot embodiments.Highlights: The low-level policy is trained on 40,115 hours of heterogeneous real-world robot data with multimodal co-training.

Selective test-time computation improves next-subtask prediction accuracy by 15–24 percentage points across in-domain and distribution-shifted settings.We evaluate real-world manipulation tasks containing 13–25 ordered steps, with episodes lasting up to 12 minutes.

Using the same low-level policy, hierarchical planning improves average closed-loop success from 27.5% to 45.0% across four long-horizon tasks.We release the official code and pretrained low-level VLA checkpoint, with high-level policy on the way.

🌐 Project page💻 Code🤗 Model checkpoint Questions and feedback are very welcome!Reply librarian-bot 2 days ago This is an automated message from the Librarian Bot.I found the following papers similar to this paper.

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