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Learning Action Manifold with Multi-view Latent Priors for Robotic Manipulation

TL;DR

Proposed a method using multi-view latent priors for action manifold learning, outperforming on LIBERO dataset.

cs.RO 🔴 Advanced 2026-05-12 29 views
Junjin Xiao Dongyang Li Yandan Yang Shuang Zeng Tong Lin Xinyuan Chang Feng Xiong Mu Xu Xing Wei Zhiheng Ma Qing Zhang Wei-Shi Zheng
AI Reader Arxiv Page Download PDF
robotic manipulation multi-view learning action manifold deep learning spatial perception

Key Findings

Methodology

The paper introduces a novel Vision-Language-Action (VLA) framework combining a multi-view diffusion model and a Geometry-Guided Gated Transformer (G3T) to address depth ambiguity from monocular input. Action Manifold Learning (AML) directly predicts actions on the valid action manifold, bypassing inefficient regression of unstructured targets.

Key Results

  • Achieved a 98.8% success rate on the LIBERO dataset, surpassing existing methods.
  • Demonstrated higher robustness and success rate on RoboTwin 2.0.
  • Achieved higher precision and stability in real-robot tasks.

Significance

This research significantly enhances spatial perception and action learning efficiency in VLA models in complex environments by introducing multi-view latent priors and action manifold learning. It addresses the depth ambiguity issue from monocular input, providing a more reliable solution for robotic manipulation.

Technical Contribution

Technical contributions include: 1) proposing a Geometry-Guided Gated Transformer to integrate multi-view features; 2) introducing Action Manifold Learning for direct action prediction, improving learning efficiency and robustness.

Novelty

This is the first to combine multi-view diffusion models with action manifold learning, addressing monocular depth ambiguity and achieving higher operational precision in complex environments.

Limitations

  • The method may perform poorly in extreme occlusion scenarios, as multi-view information may not fully eliminate occlusion effects.
  • High computational complexity in high-dimensional action spaces.

Future Work

Future work could explore more efficient multi-view information fusion methods and applications in more complex robotic manipulation tasks.

AI Executive Summary

In complex environments, robotic manipulation requires precise spatial perception and action prediction. Existing Vision-Language-Action (VLA) models face depth ambiguity issues with monocular input, limiting their application in precision tasks. This paper proposes a novel VLA framework combining a multi-view diffusion model and a Geometry-Guided Gated Transformer (G3T) to address this issue. Through Action Manifold Learning (AML), the method directly predicts actions on the valid action manifold, avoiding inefficient regression of unstructured targets and improving learning efficiency and robustness.

Experiments on datasets like LIBERO and RoboTwin 2.0 show that the method outperforms existing methods in success rate and robustness. In real-robot tasks, the method achieves higher precision and stability, demonstrating its potential for application in complex environments.

While the method performs well in most scenarios, it may have limitations in extreme occlusion cases. Future research could explore more efficient multi-view information fusion methods and applications in more complex robotic manipulation tasks.

Deep Analysis

Background

With the advancement of robotics, Vision-Language-Action (VLA) models play a crucial role in automation. However, existing models face depth ambiguity issues with monocular input, limiting their application in complex environments. Researchers have attempted to enhance spatial perception by introducing 3D foundation models or additional hardware, but these methods face challenges in cost and geometric consistency.

Core Problem

The core challenge for VLA models is the depth ambiguity issue with monocular input, which limits spatial perception capabilities and affects performance in precision tasks. Existing methods enhance spatial perception through 3D input or pre-trained models, but these face limitations in hardware cost and geometric consistency.

Innovation

The core innovations include: 1) introducing a multi-view diffusion model to synthesize novel views in latent space, reducing geometric uncertainty; 2) proposing a Geometry-Guided Gated Transformer (G3T) to align multi-view features under 3D geometric guidance; 3) implementing Action Manifold Learning (AML) to directly predict actions on the valid action manifold, improving learning efficiency.

Methodology

  • �� Use a pre-trained multi-view diffusion model to synthesize novel views in latent space, providing enriched scene context.
  • �� Introduce a Geometry-Guided Gated Transformer (G3T) to align multi-view features and filter occlusion noise through an adaptive gating mechanism.
  • �� Implement Action Manifold Learning (AML) to directly predict actions on the valid action manifold, avoiding inefficient regression of unstructured targets.

Experiments

Experiments were conducted on LIBERO, RoboTwin 2.0, and real-robot tasks to evaluate the method's success rate and robustness. Using a multi-view diffusion model and a Geometry-Guided Gated Transformer (G3T), comparative experiments were conducted in different environments to verify the method's effectiveness.

Results

On the LIBERO dataset, the method achieved a 98.8% success rate, significantly outperforming existing methods. On RoboTwin 2.0, the method demonstrated higher robustness and success rate. In real-robot tasks, the method achieved higher precision and stability.

Applications

The method can be applied to robotic manipulation tasks requiring high precision and robustness, such as industrial automation and medical robotics. Its feature of not requiring additional hardware makes it highly applicable.

Limitations & Outlook

The method may perform poorly in extreme occlusion scenarios, as multi-view information may not fully eliminate occlusion effects. Additionally, high computational complexity in high-dimensional action spaces may require exploration of more efficient computation methods.

Plain Language Accessible to non-experts

Imagine you're cooking in a kitchen with only a small window to see outside. You need to know the weather to decide whether to go out. With just this small window, you might not accurately judge the weather. This method is like installing multiple cameras for you to observe the outside from different angles, helping you judge the weather more accurately. It also filters out smudges or reflections on the window, giving you a clearer view. This way, robots can better understand their surroundings and make more accurate action decisions.

ELI14 Explained like you're 14

Imagine you're playing a game where you control a robot to complete tasks. This robot only has one camera and can't see things clearly. This method is like giving the robot multiple cameras so it can see its surroundings from different angles. This way, it can complete tasks better and won't make mistakes because it can't see clearly. Isn't that cool? It's like giving the robot super glasses to make it smarter!

Glossary

Vision-Language-Action

A model combining vision, language, and action for robotic manipulation.

Used to enhance robotic operation capabilities in complex environments.

Multi-view Diffusion Model

A model used to synthesize multi-view information, reducing depth ambiguity from monocular input.

Helps generate enriched scene context.

Geometry-Guided Gated Transformer

A model for aligning multi-view features and filtering noise.

Aligns multi-view features under 3D geometric guidance.

Action Manifold Learning

A method for direct action prediction on the valid action manifold.

Avoids inefficient regression of unstructured targets, improving learning efficiency.

LIBERO Dataset

A dataset used to evaluate robotic manipulation capabilities.

Used in experiments to verify the method's effectiveness.

Open Questions Unanswered questions from this research

  • 1 How to improve the effectiveness of multi-view information in extreme occlusion scenarios?
  • 2 How to reduce computational complexity in high-dimensional action spaces?
  • 3 How to apply this method to more complex robotic manipulation tasks?

Applications

Immediate Applications

Industrial Automation

Improves precision and efficiency in industrial environments, reducing reliance on additional hardware.

Medical Robotics

Provides more precise operational support in medical environments, enhancing safety and reliability.

Long-term Vision

Smart Home

Achieves more intelligent home robots, providing more efficient home services.

Abstract

This paper tackles spatial perception and manipulation challenges in Vision-Language-Action (VLA) models. To address depth ambiguity from monocular input, we leverage a pre-trained multi-view diffusion model to synthesize latent novel views and propose a Geometry-Guided Gated Transformer (G3T) that aligns multi-view features under 3D geometric guidance while adaptively filtering occlusion noise. To improve action learning efficiency, we introduce Action Manifold Learning (AML), which directly predicts actions on the valid action manifold, bypassing inefficient regression of unstructured targets like noise or velocity. Experiments on LIBERO, RoboTwin 2.0, and real-robot tasks show our method achieves superior success rate and robustness over SOTA baselines. Project page: https://junjxiao.github.io/Multi-view-VLA.github.io/.

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