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Five Major Industrial Robot Development Trends in the Digital Transformation Period

More intelligent robots with the help of artificial intelligence (AI)

As robots become more and more intelligent, their efficiency levels also increase. Many robots with artificial intelligence capabilities can learn to collect data, and improve actions during processes and tasks. These smarter versions can even have “self-healing” features, whereby machines can identify internal problems and repair themselves without manual intervention.

These improved levels of artificial intelligence give us a glimpse of the future of the industry, and it is possible to increase the robot labor force when working, learning, and solving problems.

Environment comes first

Organizations at all levels are beginning to prioritize the environmental impact of their daily practices, which is reflected in the types of technologies they use.

Robotics in 2021 focus on the environment because the company wants to reduce its carbon footprint while improving processes and increasing profits. Modern robots can minimize overall resource usage because their work can be more accurate and precise, eliminating human error and additional materials used to correct errors.

Robots can also assist in producing renewable energy equipment, providing opportunities for external organizations to improve energy consumption.

Cultivate human-machine collaboration

Although automation continues to improve all aspects of the manufacturing process, the increase in human-machine collaboration will continue in 2022.

Allowing robots and humans to work in a shared space provides greater synergy when performing tasks. The robot learns to respond to human actions in real-time. This safe coexistence can be seen in environments where humans may need to bring new materials to machines, change their programs, or check the operation of new systems.

The combined approach also allows for more flexible factory processes, allowing robots to complete monotonous, repetitive tasks, and allowing humans to provide necessary improvisations and changes.

More intelligent robots are also safer for humans. These robots can sense when humans are nearby and adjust their routes accordingly or take actions to prevent collisions or other potential safety hazards.

The diversity of robotics

There is no unified feeling in the robot industry in 2021. Instead, Enginners adopted various designs and materials to best suit their purpose.

Engineers are pushing the limits of existing products on the market to create more streamlined designs that are smaller, lighter, and more flexible than the predecessors. These streamlined frames also use cutting-edge innovative technology, easily programmed and optimized to achieve human-computer interaction. Using less material per unit also helps lower the bottom line and savea more production costs.

Robots enter new markets

The industrial sector has always been an early adopter of AI technology. However, the productivity provided by robots continues to increase, and many other industries have adopted exciting new solutions.

Smart factories are subverting traditional production lines, while food and beverage, textile, and plastic manufacturing have seen robotics and automation become the norm. It can be seen in all areas of the process, from advanced robots taking out baked goods from pallets and placing randomly oriented food into the packaging to monitoring accurate shades as part of textile quality control.

With the widespread adoption of the cloud and the ability to operate remotely,and with the impact of intuitive robotics, traditional manufacturing facilities will soon become productivity centers.

Customized dataset

With the acceleration of the commercialization of AI and the application of AI technologies in all walks of life, the expectation of data quality in special scenarios is getting higher and higher. High-quality labeled data would be one of the core competitiveness of AI companies.

If the general datasets used by the previous algorithm model are coarse grains, what the algorithm model needs at present is a customized nutritious meal. If companies want to further improve certain models’ commercialization, they must gradually move forward from the general dataset to create the unique one.

ByteBridge, a Human-powered and ML-powered Data Labeling Tooling Platform

ByteBridge, a data labeling tooling platform with real-time workflow management, providing a flexible data training service for the machine learning industry.

Data quality guarantee

ML-assisted capacity can help reduce human errors by automatically pre-labeling

The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy.

Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.

All results are thoroughly assessed and verified by a human workforce and machine

ByteBridge, a Human-powered and ML-powered Data Labeling Tooling Platform

In this way, ByteBridge can affirm the data acceptance and accuracy rate is over 98%.

Communication Cost Saving

On ByteBridge’s SaaS dashboard, developers can start the labeling projects by using the labeling instruction template and get the results back instantly.
From online setting labeling briefing to expert support alongside, the instruction communication is not that hard anymore.

ByteBridge Labeling Instruction Template

Control Your Own Project — 2D Images Labeling

In addition, researchers can create the data project by themselves, upload raw data, download processed results, check ongoing labeling progress simultaneously on a pay-per-task model with clear estimated time and take control over the project status.

ByteBridge, a Human-powered and ML-powered Data Labeling Tooling Platform

These labeling tools are already available on the dashboard: Image Classification, 2D Boxing, Polygon, Cuboid.

We can provide personalized annotation tools and services according to customer requirements.

NLP Service

We provide different types of NLP in E-commerce, Retail, Search engines, Social Media, etc. Our service includes Voice Classification, Sentiment Analysis, Text Recognition and Text Classification(Chatbot Relevance).

Partnered with over 30 different language-speaking communities across the globe, ByteBridge now provides data collection and text annotation services covering languages such as English, Chinese, Spanish, Korean, Bengali, Vietnamese, Indonesian, Turkish, Arabic, Russian and more.


A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.


If you need data labeling and collection services, please have a look at, the clear pricing is available.

Please feel free to contact us:




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