Robotics AI Powered by Vision Language Models and Theta Edge Computing

Robotics AI Background

The world of robotics is undergoing a rapid transformation. From autonomous vehicles and warehouse robots to delivery drones and smart industrial machines, robots are becoming an integral part of our daily lives. With advances in AI accelerating at an unprecedented pace, robotics AI is set to take another major leap in the coming decade — reshaping industries, cities, and the way we live and work.

The momentum aligns with industry forecasts: the AI robotics market is expected to 10x in the next few years growing from about $12.8 billion in 2023 to $124.8 billion by 2030, a CAGR of 38.5% (Grand View Research, 2024). The broader intelligent robotics sector is projected to expand from roughly $14 billion in 2025 to over $50 billion by 2030, growing at 29.2% annually (Markets and Markets, 2025).

Recent trends in the Robotics Market

Autonomous vehicles

Autonomous vehicles are becoming a major part of this landscape: Waymo’s fully driverless robotaxis now serve Phoenix, San Francisco, Los Angeles, Austin, and Atlanta with more than 250,000 paid rides per week, demonstrating up to 96% fewer injury-related crashes than human drivers over tens of millions of miles (Arxiv, 2025). Meanwhile, Tesla has launched a limited pilot in Austin with Model Y robotaxis, using safety monitors and a flat $4.20 fare across a 173-square-mile service area (Statesman, 2025; X, 2025), signaling the company’s entry into real-world robotaxi operations.

Consumer robotics

The global consumer robotics market is valued at USD 13.2 billion in 2025 and is projected to reach USD 40.2 billion by 2030, growing at a CAGR near 25% (Grand View Research). Market leadership is concentrated among a few firms: Ecovacs Robotics has 13–14% global share (Ecovacs), and Roborock roughly 19–22% share after nearly 38% growth (Forbes; IDC). Dreame Technology has ~11% share (IDC), while U.S. pioneer iRobot retains 13–14% share (iRobot IR).

Samsung has expanded into robotics by becoming the largest shareholder of Rainbow Robotics, acquiring a 35% stake valued at USD 181M to accelerate humanoid development (Samsung Newsroom; TechCrunch). Meanwhile, LG Electronics secured a 51% controlling stake in Bear Robotics, integrating the U.S. startup’s autonomous service robots into its portfolio (LG Newsroom; LG.com). Alongside Xiaomi with ~10% share and Anker’s Eufy brand (Anker), these companies account for more than half of the global consumer robotics market, underscoring China’s dominance but also highlighting the enduring role of U.S. and Korean players.

Technology adoption is being driven by a shift from narrow appliances toward versatile platforms. For example, Roborock’s Saros Z70, with its five-axis robotic arm, and the RockMow Z1 lawn mower illustrate this expansion (Roborock; The Verge). On the research frontier, foundation models and Vision-Language-Action (VLA) architectures like Google’s Gemini Robotics are enabling robots to interpret instructions and plan multi-step tasks (Financial Times). The rise of humanoid and embodied AI is accelerating, with Meta investing in humanoid projects (Reuters), South Korea’s K-Humanoid Alliance targeting commercialization by 2028, and China already showcasing humanoids in public deployments (The Guardian). Together with advances in dexterous manipulation, soft robotics (arXiv) and Robotics-as-a-Service (RaaS), these developments show consumer robots are moving rapidly from niche gadgets to everyday home assistants.

Industrial robots

Amazon’s Vulcan robot in Spokane, WA, is already handling over 500,000 orders and covering 75% of warehouse items using tactile sensing and AI (The Verge, 2024). DHL, working with Boston Dynamics, has deployed Stretch robots to unload trucks, doubling efficiency and preparing to scale to more than 1,000 units (WSJ, 2025).

GreenBox Systems is investing $144 million in a fully automated AI-powered warehouse in Georgia, opening in late 2025 (AP News, 2025). And Tesla’s Optimus humanoid robots are slated for internal deployment in factories by 2025, with Elon Musk projecting thousands of units by year’s en mo d, though analysts caution that hitting 10,000 units may be delayed by supply chain hurdles (The Guardian, 2024, Business Insider, 2025, Tom’s Hardware, 2025).

These deployments and forecasts show that robotics AI is leaving the lab and entering logistics hubs, warehouses, car factories and homes at scale. But with this growth comes new challenges: how can millions of robots process vast amounts of sensory information, understand their environment, and make split-second decisions?

The Rise of Vision Language Models — the Core of Robotics

Before diving deeper, it is essential to understand the core of robotics AI powered by Vision Language Models (VLM). Robotics AI is not just about movement or automation but also about understanding. For robots to interact meaningfully with humans and the environment, they need to process multiple streams of information and reason about them in real time. This is exactly what VLMs bring to the table.

With vision language models, multimodal foundation models, and classical control, robots can parse various environments from cameras, depth, and sensor data, ground language into object references and task graphs, plan sequences and recover from failure states, and learn skills quickly through imitation and reinforcement.

Projects like Google Deepmind’s Gemini Robotics-ER 1.5, SmolVLA, OpenVLA, and Figure’s Figure01 illustrate how robots learn directly from humans and take on new tasks they’ve never explicitly practiced before. This represents a major leap from rule-based automation toward embodied intelligence. Instead of requiring carefully scripted instructions for each scenario, VLA-powered robots can generalize across environments — recognizing new objects, reasoning about their context, and adjusting actions on the fly.

Why are Vision Language Models Important on and near Edge Devices

As robotics and AI evolve, the demand for real-time, intelligent decision-making has never been higher. Robots, autonomous vehicles, drones, and smart machines rely on instant understanding of their environments and this is where VLMs on edge devices become crucial.

Below are some key reasons why VLMs are so important when deployed near the edge:

1. Real-Time Responsiveness: Robots cannot afford delays. A self-driving car detecting a pedestrian, or a delivery drone recognizing a no-fly zone, needs to make a decision in milliseconds. Running VLMs directly on or near the edge ensures ultra-low latency, enabling life-critical responses without waiting for cloud servers.
2. Independence from Connectivity: Many robots operate in environments with poor or no internet access — factories, farms, underground mines, or disaster zones. By running VLMs locally on edge devices near the location such as PCs and mobile, robots can maintain full functionality even offline, making them more resilient and reliable.
3. Data Privacy and Security: Visual and sensor data from robots often involve sensitive information — whether in healthcare, manufacturing, or public spaces. Processing this data on and near the edge ensures privacy-first computing, avoiding unnecessary cloud transfers that may expose vulnerabilities.
4. Cost Efficiency: Transferring massive amounts of video and sensor data to the cloud for inference is costly. Edge-deployed VLMs reduce bandwidth usage and cloud compute costs, making large-scale robotic deployments more economically sustainable

How Theta EdgeCloud Can Power VLMs for Robotics

EdgeCloud’s distributed GPU infrastructure encompassing PCs, mobile and other IoT devices at or near the edge is ideally suited to power VLMs for robots, autonomous vehicles, drones and more:

1. Real-time Responsiveness: Edge devices will become ubiquitous. VLMs and other model queries can be executed on a nearby EdgeCloud device, whereas today’s cloud providers may only have a few data centers that are distant from the device and slower to respond.
2. Connectivity: EdgeCloud devices can provide services to other nearby edge devices through local mobile networking in areas with poor or no internet access. These include device-to-device connections, ad hoc/mesh networks and proximity 4G/5G cellular bypassing the tower for low latency and poor coverage.
3. Data Privacy and Security: EdgeCloud devices belonging to the same owner, such as their car, mobile phone and PC may share computational resources among each other without any concern for data privacy.
4. Cost Efficiency: Edge inference is vastly cheaper than cloud, especially when large AI and sensor data are required by robots, drones and more. EdgeCloud delivers unparalleled price-to-performance unmatched by traditional cloud providers.
5. Massive Parallelism: EdgeCloud offers massive parallelism needed for millions of robotic devices through its distributed architecture and voluntary node enrollment policy. In contrast, traditional data centers will always have limited GPU availability because they can’t invest billions of dollars in new GPUs unless they already have secured that business and customer commitment.

In summary, the very robots, vehicles, drones and smart machines that are being powered by EdgeCloud could become part of the network, in essence, reinforcing itself and growing the network organically. This opens us a world of possibilities and Theta’s infrastructure could become a game changer for all distributed AI systems and devices — as more robotic devices come online, more excess GPU capacity is added to EdgeCloud which in turn can power more new devices.

References :

https://arxiv.org/pdf/2502.07855v1

How Vision-Language-Action Models Powering Humanoid Robots

OpenVLA: AnOpen-Source Vision-Language-Action Mode

Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications

Robotics AI Powered by Vision Language Models and Theta Edge Computing was originally published in Theta Network on Medium, where people are continuing the conversation by highlighting and responding to this story.