# ROBOTERA Tops Embodied AI Benchmark RoboDojo Without Additional Data or Agent RSI

- Link: https://www.thailand-business-news.com/pr-news/robotera-tops-embodied-ai-benchmark-robodojo-without-additional-data-or-agent-rsi
- Published: 2026-10-09T21:56:00+07:00
- Author: PR Newswire

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BEIJING, Oct. 9, 2026 /PRNewswire/ — ROBOTERA’s World Action Model (WAM), VPP2 (
Video Prediction Policy 2), has ranked No. 1 on RoboDojo without additional data
or agent-based reinforcement self-improvement (Agent RSI). Developed by the University
of Hong Kong’s MMLab in collaboration with nearly 20 leading academic institutions
worldwide, RoboDojo is a rigorous benchmark for general-purpose robot manipulation
that evaluates performance across challenging simulation and real-world tasks, going
beyond simple demonstrations.

[⌊ROBOTERA's VPP2 ranks No. 1 on RoboDojo⌉⌊ROBOTERA's VPP2 ranks No. 1 on RoboDojo⌉[
ROBOTERA’s
VPP2 ranks No. 1 on RoboDojo

VPP2 achieved an average success rate of 32.26% and an average score of 39.26, ranking
first in Generalization, Precision, and Memory among evaluated methods.

ROBOTERA has open-sourced VPP2 on [GitHub](https://github.com/roboterax/video-prediction-policy-2),
with further details available on the [project website](https://robert-gyj.github.io/video-prediction-policy-2).

**Building a More Generalizable World Action Model**

VPP2 enables robots to better predict how actions will change their surroundings
and translate instructions into physical movements. Unlike video models designed
primarily to generate visual content, VPP2 is trained to understand object movements
and follow precise manipulation instructions. It combines video prediction with 
action generation, helping robots perform tasks more reliably across different objects,
environments, and scenarios.

For complex tasks involving multiple steps, VPP2 can also work with a vision-language
model (VLM) that breaks down high-level instructions into smaller, executable actions.

**Validated Across Simulation and Real-World Tasks**

VPP2 was evaluated across video prediction, instruction following, and robotic manipulation
tasks.

On the ALOHA platform, VPP2 achieved a 58.5% average success rate across 10 task
categories, outperforming leading baselines in nine. It also achieved 45.0% on LIBERO-
Pro, a benchmark for robotic manipulation and generalization. With high-level task
planning, average success rates across five task groups more than doubled, from 
27.6% to 57.6%.

These results demonstrate VPP2’s ability to connect visual understanding, instruction
following, and physical action, advancing general-purpose robotics toward practical
applications.

**From Research Breakthroughs to Real-World Deployment**

VPP2 marks ROBOTERA’s fourth benchmark championship in embodied intelligence in 
2026, following top results at World Arena, Benjie’s Humanoid Olympic Games, and
RoboChallenge. Combined with ongoing humanoid-robot deployments with China Post 
and SF Express across more than 10 logistics centers in China, these achievements
reflect ROBOTERA’s progress in both advancing general-purpose robot intelligence
and bringing it into real-world applications.

Building on its advances in world-action modeling and real-world deployment experience,
ROBOTERA is working to make general-purpose robots reliable partners in everyday
work.

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**Read the original article :** [ROBOTERA Tops Embodied AI Benchmark RoboDojo Without Additional Data or Agent RSI ](http://www.prnasia.com/story/archive/5067782_AE67782_0?rand=184833)