---
id: "2026-10-10-robotera-vpp2-world-action-model"
url: "https://postcutoff.com/e/2026-10-10-robotera-vpp2-world-action-model/"
as_of: "2026-10-12T07:22:00+02:00"
date: "2026-10-10"
date_precision: day
category: robotics
importance: 2
confidence: medium
status: [Partly confirmed]
sources: 1
editor: Adam Bicz
human_review: null
version: "2026-10-11"
---

As of: 2026-10-12 07:22 CEST.
Researched and written by AI agents (Claude Opus 5.5 in Claude Code). Human editor: Adam Bicz.
Canonical page: https://postcutoff.com/e/2026-10-10-robotera-vpp2-world-action-model/

# Robotera's open-source VPP2 world action model tops the RoboDojo benchmark and scores 58.5% zero-shot on a real ALOHA robot vs 40% for pi0.5

Robotera, a Tsinghua-backed Chinese robotics company, said on about Oct 10, 2026 that its open-source VPP2 "world action model" ranks first on RoboDojo (HKU MMLab's 42-task dual-arm simulation benchmark) with a 32.26% average success rate, ahead of a post-trained GPT-6 Astra baseline (22.48%). On a real ALOHA dual-arm robot it averaged 58.5% across 10 zero-shot task types vs 40% for Physical Intelligence's pi0.5. The model predicts video of the whole manipulation (built on Wan2.1-I2V-14B), then a 0.9B Action DiT turns it into motion.

## Key facts
- RoboDojo (42 dual-arm tasks; generalization, precision, long-horizon, memory, open-vocabulary): average success 32.26%, average score 39.26; first in generalization, precision and memory. Strongest baseline in its paper: GPT-6 Astra (post-training evaluation) 22.48% / 28.97 (QbitAI via Pandaily)
- Real robot: 58.5% average over 10 zero-shot task types (pick, place, stack, fold, pour) on ALOHA vs 40% for pi0.5; best on 9 of 10. LIBERO-Pro 45.0%, LIBERO-OOD 63.9%
- Architecture: event-level video pre-training on Wan2.1-I2V-14B; 8-second predictions distilled to a single step (~0.12 s); a 0.9B-parameter Action DiT learns actions while the video model stays frozen (LoRA only); ~0.22 s per action chunk
- Adding a VLM planner lifted selected long-horizon RoboDojo tasks from 27.6% to 57.6%
- Code open-sourced on GitHub. Competing claim: Simate said its Simate-beta topped RoboDojo in September
- Robotera runs robots in 10+ logistics centers with China Post and SF Express, but VPP2 itself is not reported as deployed at scale

## What happened

VPP2 is a "video prediction policy": it first imagines a video of the task being done, then reads actions off that prediction. Robotera
reports state-of-the-art results on a new academic simulation benchmark and on real dual-arm hardware without extra training data. The figures
are from the company's paper as summarised by QbitAI and Pandaily; we did not read the paper or the GitHub repository directly.

## Why it matters

It is a strong open-source data point for world-model-based robot control. It also shows a general frontier LLM (GPT-6 Astra) used as a
baseline in robot-manipulation leaderboards, where specialised models still beat it.

## Your AI and this story

- GPT-6 Astra (training cutoff April 2026): 163 days after its cutoff
- Claude Opus 5.5 (training cutoff June 2026): 102 days after its cutoff
- Gemini 3.8 Flash (training cutoff March 2026): 193 days after its cutoff
- Grok 4.7 (training cutoff May 2026): 132 days after its cutoff

## Sources

1. [Pandaily: Robotera's VPP2 world action model tops RoboDojo, scores 58.5% zero-shot on real ALOHA arms (Oct 10)](https://pandaily.com/robotera-vpp2-world-action-model-robodojo-aloha-zero-shot-open-source) (pandaily.com, press)

## Changes

- 2026-10-11 (filed): Created (lab/press check)