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What is Odyssey-3?

Odyssey-3 is a sophisticated foundational world model and dynamic system designed to predict the movement and interactions of objects in space while monitoring the progression of various scenarios over time. By leveraging an autoregressive diffusion transformer, it effectively captures representations tied to physics, dynamics, and cause-and-effect principles from a vast array of visual data, allowing it to simulate environments adeptly and formulate strategies for managing physical systems. In addition, Odyssey-3 can generate immersive environments based on user input and swiftly forecast how these settings evolve in response to actions taken by individuals or agents, incorporating both first-person and third-person navigation options alongside adaptable camera controls. The extensive knowledge of the world that it acquires can be customized for various systems through the retraining of an action decoder or policy using correlated data of observations and their respective actions. Even when provided with minimal task-specific data, Odyssey-3 has proven its effectiveness in controlling robotic arms, enabling humanoid robots, operating vehicles, and producing synchronized multi-sensory experiences. Moreover, it is adept at facilitating interactive experiences that bolster user engagement and comprehension, making it a versatile tool across multiple domains. Its ability to adapt to different contexts only further enhances its potential applications in real-world scenarios.

What is FLUX 3 Action?

FLUX 3 Action is a dynamic 7B model designed to improve action prediction in robotics and other high-latency environments. It is founded on the multimodal FLUX 3 backbone and has been rigorously pretrained on extensive datasets that include images, videos, and audio, with a significant emphasis on video materials. Following this, it enters a joint video-action training and fine-tuning phase that is specifically tailored for various robotic applications and action frameworks. By processing instructions, visual data from cameras, and robot joint angles, FLUX 3 Action can forecast motor commands while also anticipating visual results, allowing robots to execute actions, reassess their environment, and refine their plans accordingly. Unlike traditional methods that separate visual prediction from control, this model merges future video predictions with action commands, effectively using insights gained from its comprehensive video pretraining to enhance robot control strategies. Impressively, its single-step 7B model boasts a success rate of 38.3% on the RoboLab-120 benchmark, underscoring its practicality in real-world scenarios. Additionally, this novel fusion of action and perception represents a groundbreaking leap forward in the domain of robotic control, paving the way for more sophisticated and capable robotic systems. As technology advances, the implications of such an integrated approach could lead to even more intelligent and responsive robots in various applications.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Has API

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub

Company Facts

Organization Name

Odyssey

Date Founded

2023

Company Location

United States

Company Website

odyssey.systems/meet-odyssey-3

Company Facts

Organization Name

Black Forest Labs

Date Founded

2024

Company Location

Germany

Company Website

bfl.ai/models/flux-3-action

Categories and Features

AI World Models

Not specified

Foundation Models

Not specified

Categories and Features

AI Models

Not specified

AI World Models

Not specified

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