ASI<Train/> CORTEX: Brain Inspired Robotics Model
Abstract
ASI<Train/> is an initiative dedicated to developing foundational models across various verticals in artificial intelligence, with robotics being a key focus area. This white paper introduces the first model that ASI<Train/> aims to build and train, Cortex, aiming to enhance robotic capabilities through advanced AI models. Leveraging recent advancements in Large Language Models (LLMs) and multimodal AI systems, the project seeks to create robots that are not only reactive but also contextually aware and adaptive.
A significant area of exploration for ASI<Train/> is Vision-Language-Action (VLA) models. These models utilize natural language instructions to guide robot actions, enhanced by continual visual feedback from the environment. By repurposing unused tokens from core LLMs to represent robot movements, VLA models enable efficient network training and inference. Building upon existing open-source models like OpenVLA, the project plans to innovate further by delivering new state of the art models.
While VLA models are a primary focus for this divestment, ASI<Train/> remains open to developing and integrating different types of models that can enhance robotic functionality. This includes incorporating brain-inspired architectures, such as recurrent neural networks and reinforcement learning modules. These components will enable robots to adapt their actions over repeated tasks, cooperate with other robots, and plan complex, multi-step operations in changing environments.
These advancements have the potential to revolutionize industries such as manufacturing, healthcare, and hospitality, with an estimated annual financial impact of $1 billion to $3 billion. By focusing on the creation of one or more advanced AI/ML models—such as enhanced VLA models and other innovative architectures—ASI<Train/> aims to deliver tangible, deployable solutions that directly enhance robotic capabilities. This emphasis on producing concrete AI models will accelerate the development of decentralized Artificial General Intelligence (AGI) and ultimately Artificial Superintelligence (ASI), ushering in a new era of intelligent robotics.
Problem statement
Developing AI systems that can understand and interact with the physical world while processing multiple sources of information remains a significant challenge. Traditional robotics has relied on heuristic methods and preprogrammed instructions, which limit a robot's ability to adapt to dynamic environments. Recent advancements in Large Language Models (LLMs) have opened new possibilities in robotics, especially with the advent of multimodal LLMs capable of processing both text and images. These models enable robots to gather visual information from their environment and internal states, paving the way for systems that are contextually aware and adaptive.
However, despite the rapid evolution of these models, they are not yet practical for most real-world applications. Limitations such as scalability issues, real-time processing constraints, and difficulties in integrating with existing robotic control systems hinder their widespread adoption. There is a pressing need to develop advanced AI models that can effectively bridge this gap, enabling robots to better understand and navigate the complexities of the physical world while efficiently handling multiple streams of information.
Solution
The project seeks to enhance robotic capabilities by leveraging existing models and recent advancements in artificial intelligence. Recognizing the significant progress made by the open-source community, we begin by focusing on fine-tuning the OpenVLA model for specific robotic applications. OpenVLA, which builds upon LLaMA 2 and incorporates vision capabilities through technologies like CLIP and DINOv2, has demonstrated promising results in replicating and even outperforming proprietary models like Google's RT-2-X.
By customizing OpenVLA for specific robots and tasks across different industries, we aim to deliver immediate improvements in robotic functionality. This involves adjusting the model's parameters and architecture to suit the unique requirements of various robots, incorporating domain-specific data to improve performance, and optimizing the model for efficient real-time inference on different robotic platforms. This approach allows for rapid deployment and validation of the model's capabilities in real-world settings, enhancing robots with more accurate and generalized control.
Looking ahead, the next significant advancement involves building upon LLaMA 3.2, a multimodal model that integrates both vision and language capabilities. By leveraging LLaMA 3.2's advanced features, we aim to develop a new state-of-the-art foundational model that surpasses existing solutions in both performance and versatility. This model will enhance robots' ability to interpret complex visual and linguistic information simultaneously, enabling more sophisticated understanding and interaction with their environments.
An essential aspect of our long-term vision is to draw inspiration from the human brain to develop a comprehensive solution. Emulating the brain's structure and function, the approach involves combining models of various sizes and capabilities. Larger models will focus on higher-level reasoning and coarse-grain planning, while smaller models, constrained by the broader context, will handle fine movements. This hierarchical structure mirrors the brain's organization, where different regions perform specialized functions.
Implementing this approach requires training models at different scales, which the project intends to undertake. Additionally, an essential part of human learning is the ability to learn from experience. This concept inspires the integration of reinforcement learning techniques, specifically training methods that allow models to improve through interaction with their environment. The potential for further training these models in virtual environments offers significant opportunities for growth and refinement, enabling the development of robots that can adapt and learn in a manner akin to human cognition.
Financial impact
The development of advanced AI/ML models for robotics has the potential to significantly impact various industries by enhancing automation, efficiency, and innovation. An analysis of the Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM) provides insight into this potential.
Market Sizes (TAM)
Global Robotics Market: Currently valued at $71.2 billion, the robotics market is projected to grow to $165 billion by 2029. This includes industrial robots, service robots, and other robotic systems across multiple sectors.
Robotics Software Market: Valued at approximately $20 billion, it is expected to expand to $80 billion by 2032. The growth is driven by increasing demand for sophisticated software that enables advanced functionalities in robots.
Computer Vision Market: With a current value of $25 billion, the computer vision market is anticipated to reach $46.9 billion by 2030. Computer vision is critical for enabling robots to interpret and interact with their environments effectively.
Large Language Models Market: Valued at $5.6 billion, the market for large language models is expected to grow to $35 billion by 2030. This expansion reflects the broadening applications of LLMs across various industries, including robotics.
Potential Industry Impact (SAM)
Advanced AI/ML models can substantially affect several key industries:
Manufacturing: The integration of intelligent robotics can lead to automation savings and efficiency improvements valued at $500 million to over $1 billion annually on a global scale. Enhanced models can optimize production lines, reduce downtime, and improve quality control.
Healthcare: Intelligent robotic assistants could drive innovation worth $200 million to $500 million annually. Applications include surgical robots with advanced precision, patient monitoring systems, and robots assisting in rehabilitation therapies.
Service Robotics: Applications in sectors like retail, logistics, hospitality, and customer service could be valued at over $1 billion annually. Robots equipped with advanced AI models can improve inventory management, streamline supply chains, and enhance customer interactions.
Revenue Potential and Market Capture (SOM)
By developing state-of-the-art AI/ML models that outperform existing solutions, there is an opportunity to capture a meaningful share of these markets. Assuming a conservative market penetration of 1% to 5% within a few years:
Annual Revenue Potential: This translates to approximately $50 million to $100 million in the short term, with significant scaling potential as adoption increases.
Given the high-growth nature of the deep tech domain, particularly in AI and robotics, these models could achieve valuations reflecting a multiple of their revenue potential:
Valuation Estimates: With a 10x revenue multiple, the models could reach valuations ranging from $500 million to $1 billion.
Benchmarking Against Competitors
Competitors in the field provide valuable benchmarks for potential valuation and impact:
OpenAI's GPT Models: With similar foundational architectures, these models are estimated to have valuations exceeding $1 billion each. Their success underscores the substantial value attributed to cutting-edge AI capabilities.
Robotics AI Startups: Companies like Boston Dynamics often secure funding at valuations of $500 million to over $1 billion, even when focusing on niche applications within robotics. This indicates strong investor confidence in the growth potential of advanced robotics solutions.
Comparative Model Valuations
Advanced Proprietary Models: Models like Google's RT-2-X, while proprietary, are estimated to have valuations ranging from $100 million to $300 million. Their advanced capabilities set industry benchmarks but are not readily accessible for broader application.
Fine-Tuned OpenVLA: OpenVLA represents a significant community effort to reproduce and enhance advanced models. With fine-tuning, its valuation is estimated between $50 million and $200 million, reflecting its potential impact and accessibility.
State-of-the-Art Models After Extensive Training: Developing a new family of advanced models through multiple rounds of training could achieve valuations between $500 million and $1 billion. These models would offer superior performance and versatility, positioning them at the forefront of the industry.
The development of one or more advanced AI/ML models for robotics presents a substantial financial opportunity. By capturing even a small percentage of the rapidly expanding markets in robotics hardware, software, computer vision, and large language models, these models can generate significant revenue and achieve high valuations.
The potential impact across industries like manufacturing, healthcare, and service robotics underscores the importance of investing in these technologies. Enhanced models can lead to:
Increased Efficiency: Streamlining operations and reducing costs across various sectors.
Innovation Acceleration: Driving the development of new applications and services.
Market Leadership: Establishing a competitive edge in a rapidly evolving industry.
By focusing on the creation of advanced AI/ML models—such as enhanced Vision-Language-Action (VLA) models and other innovative architectures—ASI<Train/> aims to deliver tangible, deployable solutions that directly enhance robotic capabilities. This emphasis on producing concrete AI models will accelerate the development of decentralized Artificial General Intelligence (AGI) and ultimately Artificial Superintelligence (ASI), ushering in a new era of intelligent robotics and significant economic growth.
Comparable Companies
To contextualize the potential financial impact and valuation of advanced AI/ML models in robotics, it is insightful to examine comparable companies in the industry. These companies illustrate the significant market valuations and investment interests in robotics and AI-driven solutions.
Physical Intelligence
Valuation: Approximately $2.4 billion.
Focus: Development of robots for domestic assistance.
Overview: Physical Intelligence specializes in creating robotic systems designed to perform household tasks, aiming to revolutionize domestic automation. Their substantial valuation reflects investor confidence in the potential for robots to become integral in everyday home life.
Covariant
Valuation: Over $160 million raised in funding as of 2023.
Focus: AI-powered robotic solutions for warehousing and manufacturing.
Overview: Covariant develops AI software that enables robots to manipulate objects in warehouses and factories. Their technology enhances automation in fulfillment centers, improving efficiency and reducing operational costs. Covariant has formed partnerships with major companies but has not been acquired by Amazon as of the latest information.
Sereact
Valuation: Estimated at $30 million.
Focus: AI solutions for warehousing robots.
Overview: Sereact offers AI-powered systems that enable robots to perform complex tasks in logistics and warehousing environments. Their focus on niche applications demonstrates the breadth of opportunities in the robotics sector.
Wayve
Valuation: Over $200 million raised in funding, with valuations estimated around $1 billion.
Focus: Autonomous driving technologies using end-to-end machine learning.
Overview: Wayve specializes in developing AI models that enable vehicles to navigate complex urban environments using reinforcement learning and computer vision. Their approach reflects significant advancements in AI and robotics integration in the automotive industry.
Waymo
Valuation: Estimates range from $30 billion to $100 billion.
Focus: Autonomous vehicle technology.
Overview: Waymo, a subsidiary of Alphabet Inc. (Google's parent company), is a leader in self-driving car technology. With extensive testing and deployment of autonomous vehicles, Waymo's high valuation reflects the transformative potential of AI in transportation.
Tesla
Market Capitalization: Approximately $800 billion to $1 trillion as of 2023.
Focus: Electric vehicles and autonomous driving technologies.
Overview: Tesla is at the forefront of integrating AI and robotics in the automotive industry. Analysts from firms like ARK Invest have projected optimistic future valuations for Tesla based on its advancements in autonomous driving and robotics initiatives. Projections suggest a potential market capitalization reaching $8.2 trillion by 2029, highlighting the immense economic potential associated with advancements in AI and robotics.
Analysis
The valuations and activities of these companies demonstrate the substantial financial opportunities in developing advanced AI and robotics technologies:
Investor Confidence: High valuations and significant funding rounds indicate strong investor belief in the future growth of AI and robotics applications across various sectors.
Strategic Partnerships and Acquisitions: Collaborations and acquisitions in the industry highlight the strategic importance of robotics in enhancing operational efficiency and competitiveness.
Diverse Applications: The range of focus areas—from domestic robots and warehousing automation to autonomous vehicles—underscores the vast potential market for advanced AI/ML models.
Economic Impact: Projections for companies like Tesla and Waymo illustrate how advancements in AI and robotics can significantly influence market capitalization and drive economic growth.
By comparing ASI<Train/>'s potential developments to these industry leaders, it becomes evident that creating advanced AI/ML models for robotics can lead to substantial valuations and market impact. The focus on delivering state-of-the-art models positions ASI<Train/> to capitalize on the growing demand for intelligent robotic solutions.
The success of companies like Physical Intelligence, Covariant, and Wayve showcases the market's readiness to adopt advanced AI models that enhance robotic capabilities. ASI<Train/>'s initiative to develop one or more advanced AI/ML models—such as enhanced Vision-Language-Action (VLA) models and brain-inspired architectures—aligns with industry trends and investor interests.
Communities & organizations interested in the model
The advanced AI models for robotics proposed by ASI<Train/> are poised to capture the attention of a diverse array of communities and organizations. By engaging with these communities, ASI<Train/> aims to foster a collaborative environment that accelerates innovation and adoption.
Communities
OpenVLA Community The OpenVLA (Vision-Language-Action) project on GitHub represents an active community dedicated to advancing multimodal AI models for robotics. This group of developers and researchers focuses on creating open-source models that enable robots to interpret visual inputs and execute language-guided actions. ASI<Train/>'s work aligns with the interests of this community, presenting opportunities for future collaboration and mutual enhancement of models.
Robotics Forums and Online Communities
Reddit's r/robotics: A dynamic platform where professionals, enthusiasts, and hobbyists discuss the latest developments in robotics. The community is known for its collaborative problem-solving and knowledge sharing, making it a potential hub for feedback and dissemination of ASI<Train/>'s advancements.
Robot-Forum.com: An international forum that brings together robotics professionals to exchange technical insights and discuss industry trends. Engaging with this forum could provide valuable perspectives on practical challenges and industry needs.
RoboDK Forum: Focused on robot simulation and offline programming, this community comprises developers and engineers working on robotic applications. Their expertise could be beneficial in refining and testing the practical applications of the models.
Neuro-AI and Robotics Groups
Neuro-AI Robotics Research Consortium: This group explores the intersection of neuroscience, artificial intelligence, and robotics, investigating brain-inspired models and learning algorithms. Their research interests align closely with ASI<Train/>'s vision of integrating brain-inspired architectures, suggesting potential for future collaboration.
Universities
Leading universities and research centers are at the forefront of robotics and AI innovation.
University of California, Los Angeles (UCLA)
Robotics & Mechanisms Laboratory (RoMeLa): Specializing in robot locomotion and manipulation, RoMeLa explores autonomous systems and human-robot interaction. ASI<Train/>'s models could be of interest for research projects focusing on enhancing robotic mobility and adaptability.
Stanford University
Stanford Robotics Center (SRC): Engaging in interdisciplinary research, SRC covers robotic perception, learning, and human-robot interaction. The models developed by ASI<Train/> might complement ongoing research initiatives, potentially leading to future collaborative efforts.
Massachusetts Institute of Technology (MIT)
Department of Electrical Engineering and Computer Science (EECS)
Computer Science and Artificial Intelligence Laboratory (CSAIL): Known for pioneering work in AI and robotics, MIT's departments could find ASI<Train/>'s advancements relevant to their research, especially in integrating AI models with robotic systems.
University of California, Berkeley
Department of Mechanical Engineering
Department of Electrical Engineering and Computer Sciences (EECS): With research areas including machine learning, control systems, and bio-inspired robotics, UC Berkeley might have potential interest in the brain-inspired architectures proposed by ASI<Train/>.
Imperial College London
Robot Intelligence Lab: Focusing on robot learning, perception, and decision-making, the lab's research objectives align with ASI<Train/>'s goals. This presents possibilities for future academic collaborations or knowledge exchange.
Companies
The development of advanced AI/ML models for robotics, such as enhanced Vision-Language-Action (VLA) models, holds significant appeal across various industries. Companies that could benefit from integrating these models into their operations include:
Manufacturing and Industrial Automation:
ABB Robotics: A leader in industrial automation, ABB is leveraging AI to make robotics smarter and easier to program across all industries they serve.
KUKA: Specializes in robotics and automation solutions for diverse industries, including automotive and electronics.
Logistics and Warehousing:
Amazon Robotics: Amazon has introduced several warehouse robots designed to improve efficiency and reduce employee injuries, such as the autonomous mobile robot Proteus and the humanoid Digit.
Covariant: Develops AI-powered robotic solutions for warehousing and manufacturing, enabling robots to manipulate objects in warehouses and factories.
Healthcare and Medical Robotics:
Intuitive Surgical: Known for the da Vinci surgical system, integrating advanced robotics with AI for minimally invasive procedures.
Medtronic: Develops robotic-assisted surgical technologies and AI-driven healthcare solutions.
Agriculture:
Carbon Robotics: Creates agricultural robots powered by AI, specializing in weed control with products like the LaserWeeder.
Service and Hospitality:
SoftBank Robotics: Develops humanoid robots like Pepper, designed for customer interaction in retail and hospitality settings.
Savioke: Provides service robots for the hospitality industry, enhancing guest experiences through automation.
Automotive:
Tesla: Integrates AI and robotics in manufacturing and autonomous driving technologies.
Waymo: Focuses on autonomous vehicle technology, utilizing advanced AI models for navigation and decision-making.
Construction:
Built Robotics: Develops AI guidance systems to transform heavy equipment into autonomous robots for construction.
These companies are at the forefront of integrating advanced AI models into their operations, aiming to enhance efficiency, safety, and innovation across various sectors.
Details of the methodology
The ASI<Train/> project will progress through a series of stages, each demonstrating incremental improvements in developing advanced AI models for robotics. This staged approach allows for systematic development, testing, and refinement of models, ensuring that each phase builds upon the successes of the previous ones.
Model Training Process
Each stage involves a comprehensive model training process consisting of the following steps:
Data Preparation
Description: Collection, preprocessing, and augmentation of datasets necessary for training. This may involve generating synthetic data to enhance diversity and cover scenarios not readily available in real-world data.
Objective: To ensure the model has access to high-quality, diverse data that represents the complexities of real-world environments.
Model Preparation
Description: Selection and configuration of model architectures suitable for the task. This may include adapting existing models or developing new architectures to meet specific requirements.
Objective: To establish a solid foundation for training by choosing models that are best suited for learning the desired behaviors.
Compute Preparation
Description: Setting up the computational infrastructure needed for training, including hardware (GPUs, TPUs) and software environments. Optimization of resources to handle the computational demands efficiently.
Objective: To ensure that the training process is efficient and scalable, minimizing bottlenecks and resource constraints.
Training
Description: Execution of the training algorithms using the prepared data and models. This involves adjusting hyperparameters, monitoring training progress, and employing techniques to prevent overfitting.
Objective: To iteratively improve the model's performance through exposure to data, enabling it to learn the underlying patterns and behaviors.
Evaluation
Description: Assessment of the trained model's performance using validation datasets and predefined metrics. Analysis of strengths, weaknesses, and areas requiring improvement.
Objective: To determine the model's effectiveness and readiness for deployment or further development.
After the evaluation step, the process is iterative. Based on the results, the cycle may return to Compute Preparation (Step 3) or even Data Preparation (Step 1) to refine and enhance the model's capabilities. This feedback loop is essential for continuous improvement and adaptation to new challenges.
Project progression
Phase 1
The initial phase establishes the groundwork by leveraging state-of-the-art generalist robotics systems based on Vision-Language-Action (VLA) models. The focus is on understanding and utilizing existing architectures to create a baseline for further development.
Key Components:
OpenVLA Architecture
Description: Combines the Llama 2 7B parameter language model with two image embedding models (DINOv2 and SigLIP) to train a 7-dimensional robot action model.
Dataset: Utilizes the Open X-Embodiment Dataset, containing data for various robot arms controlled by uniform action control vectors.
Functionality: The model processes visual and textual inputs to predict sequences of action vectors, enabling robots to execute tasks based on multimodal information.
Process:
Integration of Vision and Language Models: The Llama 2 LLM is augmented with visual embeddings from vision transformer networks, creating a multimodal model capable of understanding complex inputs.
Supervised Learning: The model is trained using supervised learning to map inputs to desired actions, establishing a foundation for robotic control.
Objective:
To establish a strong starting point by building upon proven architectures, setting the stage for subsequent advancements.
Phase 2
In the second phase, the project aims to develop an improved foundational model by leveraging the latest advancements in multimodal Large Language Models (LLMs).
Key Components:
Llama 3.2 11B Model
Description: An 11-billion-parameter multimodal model incorporating both vision and language capabilities, trained on internet-scale data.
Advantages: Offers enhanced understanding and generalization due to its larger size and extensive training data.
Process:
Model Adaptation: Utilize Llama 3.2 11B's integrated vision-language capabilities to create a superior VLA model without the need for separate conditioned language models.
Specialization for Robotics: Adapt the model to the robotics domain, ensuring it can process relevant inputs and produce effective action outputs.
Objective:
To produce a better foundational VLA model that can be specialized for specific robots, enabling the development of a fleet of specialized models through iterative refinement.
Phase 3
Building upon the foundational model from Phase 2, the third phase focuses on refining the model for specific use cases using simulated environments and exploring advanced learning methods.
Key Components:
Simulation Environment
Tool: NVIDIA Isaac Sim and other simulation environments
Purpose: Provides a realistic and controllable environment for training and testing models without the constraints of physical hardware.
Proprietary Data
Description: Data collected from specific use cases, enhancing the model's relevance and performance in targeted applications.
Process:
Fine-Tuning: Adjust the model's parameters using data from simulated environments to improve performance on specific tasks.
Reinforcement Learning: Investigate and implement reinforcement learning techniques, allowing the model to learn optimal behaviors through trial and error and reward signals.
Objective:
To enhance the model's adaptability and effectiveness in real-world scenarios by leveraging simulation and advanced learning methods.
Phase 4
The fourth phase involves scaling the model's capabilities by training a larger, more powerful version based on the Llama 3.2 70B model.
Key Components:
Llama 3.2 70B Model
Description: A 70-billion-parameter model offering significantly greater capacity for learning and generalization.
Process:
Methodology Reuse: Apply the techniques and knowledge gained from Phase 2 to the larger model, addressing the challenges associated with increased computational requirements.
Training Optimization: Implement strategies to efficiently train the large model, such as distributed computing and advanced optimization algorithms.
Objective:
To develop a state-of-the-art VLA model with enhanced performance and versatility, pushing the boundaries of robotic intelligence.
Phase 5
The final phase draws inspiration from the human brain to create a hierarchical system, integrating multiple models to achieve efficient and real-time robotic control.
Key Components:
Hierarchical Model Structure
Coarse-Level Models: Handle high-level planning, decision-making, and abstract reasoning.
Fine-Level Models: Manage precise control, motor functions, and detailed execution of tasks.
Integration Mechanism
Communication Protocols: Establish methods for models at different levels to interact seamlessly.
Optimization: Ensure that the combined system operates efficiently without redundant computations.
Process:
Model Fine-Tuning: Adjust models to focus on their respective levels of detail, optimizing them for their specific roles.
System Integration: Combine the models into a cohesive unit, mirroring the brain's ability to coordinate multiple functions simultaneously.
Performance Enhancement: Implement techniques to achieve real-time performance, such as parallel processing and efficient resource allocation.
Objective:
To create an advanced robotic control system that emulates human cognitive processes, enabling robots to perform complex tasks with high efficiency and adaptability.
Available Data Sets
The success of the ASI<Train/> project heavily relies on the availability of high-quality datasets that provide diverse and rich information for training advanced AI models in robotics. Utilizing both open-source datasets, proprietary datasets, and synthetic data generated through simulation environments enhances the models' ability to generalize and perform effectively in real-world scenarios.
Open-Source Datasets
Leveraging publicly available datasets allows for a broad foundation of knowledge, covering various robotic embodiments, tasks, and environments. Below are key datasets that are instrumental for different stages of the project:
Open X-Embodiment
Description: Contains 2.4 million trajectories across 22 different robotic embodiments.
Content: The dataset includes diverse robotic actions and interactions, providing extensive coverage of motion patterns and control strategies.
BridgeData V2
Description: Comprises 60,000 trajectories focusing on bridging the gap between simulated and real-world data.
Content: Offers paired data that connects simulation results with real-world observations.
RoboNet
Description: Contains 82,000 trajectories collected from a variety of robotic platforms.
Content: Features multi-task demonstrations across different robots and environments.
RH20T
Description: A dataset of 110,000 trajectories focusing on robotic hand manipulation tasks.
Content: Includes detailed recordings of grasping, manipulation, and dexterous hand movements.
Robo360
Description: Consists of 2,000 trajectories with 360-degree views of robotic tasks.
Content: Provides comprehensive visual data capturing full environmental contexts.
Other Relevant Datasets
Google's Robotic Manipulation Dataset
Description: Offers a large collection of robotic manipulation experiences.
Yale-CMU-Berkeley (YCB) Object and Model Set
Description: Provides detailed 3D models and physical properties of common objects.
Synthetic Data Generation
In addition to open datasets, synthetic data plays a crucial role in expanding the training data and covering scenarios that are difficult to capture in real life.
NVIDIA Isaac Sim
Description: A powerful simulation platform that enables the creation of photorealistic, physics-accurate virtual environments for robotics.
Capabilities:
Realistic Simulation: Provides high-fidelity rendering and physics simulation, allowing for accurate replication of real-world conditions.
Scalable Data Generation: Facilitates the generation of large volumes of diverse training data, including edge cases and rare events.
Customizable Environments: Users can design specific scenarios, tasks, and environments tailored to the project's needs.
Usage:
Phase 3: Key tool for fine-tuning models using simulated environments. Enables the exploration of reinforcement learning techniques by providing a safe and controlled setting for agents to learn from interactions.
Reinforcement Learning: Supports the implementation of reinforcement learning algorithms by simulating reward mechanisms and feedback loops.
Validation and Testing: Offers a platform for preliminary testing of models before deploying them on physical hardware, reducing risks and costs.
Benefits of Synthetic Data
Data Augmentation: Enhances the diversity of training data, improving the model's robustness and generalization.
Controlled Variables: Allows for manipulation of specific environmental factors to study their impact on model performance.
Accessibility: Eliminates the need for physical robots during early development stages, making the process more accessible and cost-effective.
Model training and data curation cost
Developing advanced AI models for robotics is a resource-intensive endeavor that involves significant computational costs. This section outlines detailed cost estimates for each stage of the ASI<Train/> project, providing a clear understanding of the financial requirements and considerations involved.
Baseline Training Reference
To establish a foundation for cost estimations, the training of the OpenVLA model serves as a benchmark. The OpenVLA model was trained on the Open X-Embodiment dataset, utilizing 970,000 trajectories from this comprehensive dataset. The training process was conducted on a cluster of 64 Nvidia A100 GPUs over a period of 15 days, amounting to approximately 23,040 GPU-hours (64 GPUs × 360 hours).
Compute Infrastructure
For the training requirements of the ASI<Train/> project, the proposed computational setup is as follows:
Compute Instance Configuration:
8 Nvidia H100 GPUs
32 Virtual CPUs (vCPUs)
128 GB RAM
10 TB External Storage + 1 TB Boot Disk
This configuration is estimated to cost approximately $16,000 per month, translating to an hourly rate of about $22. The choice of Nvidia H100 GPUs is strategic, as they offer approximately a 5x speed improvement over the previous generation A100 GPUs, thereby reducing overall training time and costs.
Phase 1: Fine-Tuning the OpenVLA Model
In Phase 1, we aim to fine-tune the existing OpenVLA model to adapt it to specific robotic applications.
Assumptions:
Fine-tuning requires 10% of the computational resources used for full training.
Nvidia H100 GPUs offer a 5x speed improvement over A100 GPUs.
Compute Requirements:
Original OpenVLA training compute: 23,040 GPU-hours.
Fine-tuning compute requirement: 2,304 GPU-hours (10% of 23,040 GPU-hours).
Adjusted for H100 GPU performance: 461 GPU-hours (2,304 GPU-hours ÷ 5).
Cost Calculation:
Total Cost: 461 GPU-hours × $2.75 per GPU-hour = $1,268.
Result:
The estimated cost for fine-tuning the OpenVLA model in Phase 1 is approximately $1,300. Various finetuning and adaptations will cost N * 1300 USD.
Phase 2: Training a New Robotic VLA Model
Phase 2 involves training a new Vision-Language-Action (VLA) model using the LLaMA 3.2 11B multimodal model as the backbone, leveraging recent advancements to improve performance.
Dataset Specifications:
2M trajectory (episode)
On average 150 timestep per episode
Image size: 384x384
Token Estimations:
Tokens per Image: Approximately 500.
Tokens per Natural Language Instruction: 73 (average).
Tokens per Trajectory: Approximately 86,000 (500 tokens × 150 timesteps + 73 tokens).
Training Parameters:
Training Tokens per Epoch: 86 billion (86,000 tokens × 1 million trajectories).
Number of Epochs: 27.
Total Training Tokens: Approximately 2.32 trillion (86 billion tokens × 27 epochs).
Compute Performance:
Throughput per H100 GPU: Approximately 3,000 tokens per second (using LoRA rank 64 method).
Total Compute Time Required: Approximately 214,814 GPU-hours (2.32 trillion tokens ÷ (3,000 tokens/sec × 3,600 sec/hour)).
Distributed Training Setup:
Compute Instances: 128 machines (each with 8 H100 GPUs).
Total GPUs: 1,024 GPUs.
Network Overhead: Assuming a 15% performance hit due to inter-node communication.
Adjusted GPU-hours: 214,814 GPU-hours × 1.15 ≈ 247,036 GPU-hours.
Total Training Time: 247,036 GPU-hours ÷ 1,024 GPUs ≈ 241 hours (about 10 days).
Cost Calculation:
Total Cost: 247,036 GPU-hours × $2.75 per GPU-hour ≈ $679,599.
Result:
The estimated cost for training the new VLA model in Stage 2 is approximately $680,000.
Phase 3: Fine-Tuning with Simulated Data
In Phase 3, we focus on fine-tuning the model using simulated environments and, when necessary, proprietary data collected from real robots to adapt it to specific use cases.
Data Generation:
Simulation Data:
Utilization: Leveraging simulation tools like NVIDIA Isaac Sim to generate synthetic data.
Cost: The cost of generating data through simulation is relatively low and can be considered negligible compared to training costs.
Proprietary Data with Real Robots:
Utilization: Collecting data using real robotic hardware to capture specific use cases and environments.
Cost: Generating proprietary data with real robots is expensive, estimated between $10,000 and $100,000 per 10,000 trajectories, depending on complexity and resource requirements.
Training Costs:
Fine-Tuning with Simulated Data:
Compute Requirements: Due to the smaller dataset and reduced number of epochs (e.g., 4 epochs), the compute cost is relatively low.
Cost Estimate: Approximately $1,000 per 10,000 trajectories.
Fine-Tuning with Proprietary Data from Real Robots:
Compute Requirements: Similar to fine-tuning with simulated data.
Cost Estimate: Approximately $1,000 per 10,000 trajectories, plus the higher data generation costs.
Results:
Using Simulated Data Only:
Total Cost: Primarily the training cost of approximately $1,000 per 10,000 trajectories.
Data Generation Cost: Negligible.
Using Proprietary Data from Real Robots:
Data Generation Cost: Between $10,000 and $100,000 per 10,000 trajectories.
Total Cost: Data generation cost plus training cost, totaling $11,000 to $101,000 per 10,000 trajectories.
Conclusion:
Simulation Advantage: Fine-tuning the model using simulated data is cost-effective, allowing for adaptation to specific use cases at a relatively low cost.
Proprietary Data Consideration: When real-world proprietary data is necessary, the data collection costs significantly increase the overall expenses.
Result:
The estimated cost for fine-tuning with simulated data is approximately $1,000 per 10,000 trajectories, plus data generation costs.
Phase 4: Scaling Up to the LLaMA 3.2 70B Model
Phase 4 involves training a larger VLA model based on the LLaMA 3.2 70B model.
Scaling Factors:
The 70B model is 7 times larger than the 11B model, requiring proportionally more compute.
Compute Requirements:
Total GPU-hours needed: 214,814 GPU-hours × 7 ≈ 1,503,698 GPU-hours.
Adjusted for Network Overhead: 1,503,698 GPU-hours × 1.15 ≈ 1,729,252 GPU-hours.
Total Training Time:
Total GPUs: 1,024 GPUs.
Training Duration: 1,729,252 GPU-hours ÷ 1,024 GPUs ≈ 1,689 hours (about 70 days).
Cost Calculation:
Total Cost: 1,729,252 GPU-hours × $2.75 per GPU-hour ≈ $4,755,193.
Scaling Consideration:
Doubling the dataset to 2 million trajectories would roughly double the compute cost to $9.5 million.
Result:
The estimated cost for training the 70B model in Phase 4 is approximately $4.76 million.
Risks
There are numerous risks associated with the robotics project.
It may not be possible to replicate the results of the initial training of OpenVLA.
Non-disclosed hardware issues such as the interconnect speed between nodes may greatly increase the cost of training.
It is possible that larger, deeper and more complex backbone LLMs may require substantially more training time and/or data than the smaller architectures.
The project is reliant on simulated data comprising synthetic images, feedback and responses. It may be too expensive to obtain this data and it may also not be as effective as real-world experimental data.
The algorithm’s performance may be insufficient for real-world applications and/or the inference costs may be excessively high or too slow for edge-located devices.
Robotics is a highly competitive field, and it is possible that a new approach to training robotic agents may greatly surpass the current state-of-the-art.
Larger models like LLaMA 3.2 70B may face scalability and inference latency challenges.
References
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