According to Cognitive Market Research, the global Cloud Robotics market size will be $19.9 Billion by 2030, whereas its compound annual growth rate with be 19.57% from 2023 to 2031.

The global Cloud Robotics market will expand significantly by XX% CAGR between 2023 to 2031.
North America held the major market of more than XX% of the global revenue with a market size of USD XX million in 2023 and will grow at a compound annual growth rate (CAGR) of XX% from 2023 to 2031.
Industrial Robots held the highest Cloud Robotics market revenue share in 2023.

Market Dynamics – Key Drivers of the Cloud Robotics Market

Growing Industry Demand for Automation Is Anticipated to Fuel Cloud Robotics Market Growth

Since numerous industrial processes, like painting, machining, material handling, welding, and assembling, are now being replaced by automated robots, the market for cloud robotics is expanding. These robots surpass human performance in terms of accuracy, speed, and precision by using electrical, mechanical, and software systems to carry out the assigned task. This makes them more competitive in the market and draws in customers looking for cutting-edge technology. Furthermore, because automation in industrial robots is so fast and precise in organized manufacturing environments, it has been incredibly successful. Robots may now access datasets remotely to support a variety of functions thanks to the cloud's ability to connect them. Therefore, during the projected period, industry automation is anticipated to fuel the expansion of the worldwide cloud robotics market.

For instance, according to an article from ABB, Enhanced output, accuracy, and excellence Industries can improve the efficiency, accuracy, and caliber of their manufacturing by checking items throughout the process rather than after it is finished and by integrating the process into the production area rather than having it situated separately. Significant cost reductions and quality control for all completed items are also achieved. Additionally, by streamlining the control and supervision operator's workload, these solutions encourage teamwork.

Integration of Artificial Intelligence to Propel the Cloud Robotics Market Further

The market for cloud robotics is driven by ongoing research and development in artificial intelligence and related fields. Robots with strong capabilities are made possible by artificial intelligence, which also lowers operating expenses. With an intelligent operating system (OS), which consists of a data center, communication support, information processing, knowledge base, etc., it is now feasible to create cheaper, lighter, and smarter robots. Furthermore, in order for robots to think like humans, they require enormous amounts of computational power and resources. Artificial intelligence advances can facilitate resource extraction from multiple data centers by robots, thereby dispersing demand. Therefore, it is anticipated that these factors will fuel market expansion in the near future.

For instance, according to PWC's estimates, artificial intelligence (AI) might add USD 15.7 trillion to the global economy by 2030.3. Furthermore, according to our analysis, certain local economies could see a 26% increase as a result of the use of AI. But in order to really benefit from AI, organizations must begin to see technology as a necessity as opposed to a luxury. The firm must integrate AI into its core operations. In the process, companies must acknowledge that increasing demand in developing nations like India can be a more pertinent advantage than cost savings from automation. Therefore, considering the abundance of highly qualified personnel, AI efforts should be focused on helping people get their businesses ready for the future.

Market Dynamics – Key Restraints of the Cloud Robotics Market

High Cost of Implementation and R&D Limit the Cloud Robotics Market

A cloud-integrated robot requires a hefty initial capital investment to be manufactured. Furthermore, manufacturers must make significant investments in R&D to support cutting edge technology and bespoke applications. This makes such a robot extremely costly and unaffordable for small businesses or end consumers. Thus, during the course of the forecast period, high startup costs and R&D expenses are anticipated to limit the growth of the worldwide cloud robotics market.

For instanc...

REASSEMBLE: A Multimodal Dataset for Contact-rich Robotic Assembly and Disassembly

📋 Introduction

Robotic manipulation remains a core challenge in robotics, particularly for contact-rich tasks such as industrial assembly and disassembly. Existing datasets have significantly advanced learning in manipulation but are primarily focused on simpler tasks like object rearrangement, falling short of capturing the complexity and physical dynamics involved in assembly and disassembly. To bridge this gap, we present REASSEMBLE (Robotic assEmbly disASSEMBLy datasEt), a new dataset designed specifically for contact-rich manipulation tasks. Built around the NIST Assembly Task Board 1 benchmark, REASSEMBLE includes four actions (pick, insert, remove, and place) involving 17 objects. The dataset contains 4,551 demonstrations, of which 4,035 were successful, spanning a total of 781 minutes. Our dataset features multi-modal sensor data including event cameras, force-torque sensors, microphones, and multi-view RGB cameras. This diverse dataset supports research in areas such as learning contact-rich manipulation, task condition identification, action segmentation, and more. We believe REASSEMBLE will be a valuable resource for advancing robotic manipulation in complex, real-world scenarios.

{"Hama1": [
    [x ,y, z],
    [qx, qy, qz, qw]
 ], 
 "Hama2": [
    [x ,y, z],
    [qx, qy, qz, qw]
 ], 
 "DAVIS346": [
    [x ,y, z],
    [qx, qy, qz, qw]
 ], 
 "NIST_Board1": [
    [x ,y, z],
    [qx, qy, qz, qw]
 ]
}

[x, y, z] represent the position of the object, and [qx, qy, qz, qw] represent its orientation as a quaternion.

The HDF5 (.h5) format organizes data into two main types of structures: datasets, which hold the actual data, and groups, which act like folders that can contain datasets or other groups. In the diagram below, groups are shown as folder icons, and datasets as file icons. The main group of the file directly contains the video, audio, and event data. To save memory, video and audio are stored as encoded byte strings, while event data is stored as arrays. The robot’s proprioceptive information is kept in the robot_state group as arrays. Because different sensors record data at different rates, the arrays vary in length (signified by the N_xxx variable in the data shapes). To align the sensory data, each sensor’s timestamps are stored separately in the timestamps group. Information about action segments is stored in the segments_info group. Each segment is saved as a subgroup, named according to its order in the demonstration, and includes a start timestamp, end timestamp, a success indicator, and a natural language description of the action. Within each segment, low-level skills are organized under a low_level subgroup, following the same structure as the high-level annotations.

📁

The splits folder contains two text files which list the h5 files used for the traning and validation splits.

📌 Important Resources

The project website contains more details about the REASSEMBLE dataset. The Code for loading and visualizing the data is avaibile on our github repository.

📄 Project website: https://tuwien-asl.github.io/REASSEMBLE_page/
💻 Code: https://github.com/TUWIEN-ASL/REASSEMBLE

RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation

Accepted by Robotics: Science and Systems (RSS) 2025.

  💾 Overview of RoboMIND 💾







  🤖 Composition of RoboMIND 🤖

We present RoboMIND (Multi-embodiment Intelligence Normative Dataset and Benchmark for Robot Manipulation), a comprehensive dataset featuring 107k real-world demonstration trajectories spanning 479 distinct tasks and involving 96 unique object classes. The… See the full description on the dataset page: https://huggingface.co/datasets/x-humanoid-robomind/RoboMIND.

China Industrial Production: Industrial Robot data was reported at 61,906.000 Unit in Mar 2025. This records a decrease from the previous number of 71,382.000 Unit for Dec 2024. China Industrial Production: Industrial Robot data is updated monthly, averaging 20,761.000 Unit from Nov 2015 (Median) to Mar 2025, with 93 observations. The data reached an all-time high of 71,382.000 Unit in Dec 2024 and a record low of 1,089.000 Unit in Nov 2015. China Industrial Production: Industrial Robot data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BA: Industrial Production.