ManiBox

ManiBox is a novel approach in robotic grasping that addresses the challenge of spatial generalization in manipulation tasks. Traditional models often struggle with accurately positioning objects for grasping due to the extensive data requirements needed for spatial understanding. ManiBox overcomes these challenges by employing a state-based policy generalization method within a simulation-based teacher-student framework. The teacher policy generates scalable simulation data using bounding boxes, which uniquely determine the spatial positions of objects. The student policy then uses these low-dimensional spatial states to enable zero-shot transfer to real robots. This approach significantly improves spatial grasping generalization and adaptability to diverse objects and backgrounds. Empirical studies have shown that ManiBox's performance scales with data volume according to a power law, and the success rate of grasping follows Michaelis-Menten kinetics relative to data volume, indicating a saturation effect as data increases.

Category: Artificial Intelligence

Subcategory: Robotics

Tags: robotic graspingspatial generalizationsimulation-based learning

AI Type: Machine Learning

Programming Languages: Not specified

Frameworks/Libraries: Not specified

Application Areas: Robotic manipulation tasks

Manufacturer Company: Not specified

Country: Not specified

Algorithms Used

Teacher-student framework, bounding box guidance

Model Architecture

Not specified

Datasets Used

Simulation data

Performance Metrics

Spatial grasping generalization, adaptability

Deployment Options

Not specified

Cloud Based

On Premises

Features

Zero-shot transfer, improved spatial generalization

Enterprise

Hardware Requirements

Not specified

Supported Platforms

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Interoperability

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Security Features

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Compliance Standards

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Certifications

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Open Source

Source Code URL

https://thkkk.github.io/manibox

Documentation URL

http://Not specified

Community Support

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Contributors

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Training Data Size

Not specified

Inference Latency

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Energy Efficiency

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Explainability Features

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Ethical Considerations

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Known Limitations

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Industry Verticals

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Use Cases

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Customer Base

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Integration Options

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Scalability

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Support Options

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SLA

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User Interface

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Multi-Language Support

Localization

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Pricing Model

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Trial Availability

Partner Ecosystem

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Patent Information

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Regulatory Compliance

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Version

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Website URL

http://Not specified

Service Type

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Has API

API Details

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Business Model

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Price

0.00

Currency

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License Type

Not specified

Release Date

01/01/1970

Last Update Date

01/01/1970

Contact Email

Not specified

Contact Phone

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Social Media Links

http://Not specified

Other Features

Not specified

Published

Yes