An exploration of how foundation models may improve humanoid robot perception, decision-making, and task execution, and address limits of earlier training methods.
The background and significance of the development of large models of humanoid robots with intelligence.
Today, the research and application of human-shaped robots is a hot field. The essence of intelligence is that artificial intelligence is driven by high-technology supply-driven productivity paradigm re-engineering. Traditional human-form robots face constraints in their development and have yet to achieve real intelligence.
Advances in the technology of large models in this cycle have created new opportunities to address these problems. Large models have significantly increased the technological supply of robotic perception, decision-making, control systems, and have broadened robotic applications while lowering the threshold for software development. As a self-conforming intelligence, smart robots can improve the supply of labour in terms of mindsets and modes of behaviour, replace human beings in special scenarios, and be applied on a large scale in long-tailed, flexible manufacturing scenarios, with the promise of freeing productivity and reshaping the productivity paradigm to a great extent. Thus, the development of large models of humanoid robots with intelligence has important relevance and far-reaching prospects for development.
Remodeling large models of humanoid robotic algorithms
Large models that have emerged in recent years are expected to reshape algorithmic models for humanoid robots. In cases where software algorithm performance is inadequate, large models will be first mounted on service robots for human interaction. Subsequent image-text-action (VLA) large models can integrate software algorithms with three modules: perception, decision-making planning, control, reduction of inter-module “hard friction” and Prompt friction, and complete realization of end-to-end models.
In the case of Optimus, which was launched by Tesla, it boosted the heat of its own humanoid robots. Although humanoid robots have more parts to inherit from industrial robots, the core ecological position is occupied by cross-border giants. This is because cross-border giants have a strong AI capability to maintain the core edge of humanoid robotic software at the algorithm, algorithm, and data levels. A large model, or a necessary option to be smart, a smart one, or a pictogram of a large model in the real world, would fundamentally change the algorithm development path of humanoid robots and enhance their performance and application.
Use of large models in humanoid robotic sensory interaction
Optimization of audio-visual fusion sensing interaction
To achieve intellectual interaction with robots, perception is the first step. The robot’s interaction is still based on microphone arrays, with traditional algorithmal beams forming too wide an angle, making the noise from both sides of the speaker easily recorded, making robots virtually unusable in complex environments. By adding a neural network, it significantly limits the positive angle from 60 degrees to 30 degrees, effectively inhibiting both sides of the noise and achieving better interaction in the worse scenario.
However, human noise is more common and difficult in the process of actual production. In addition to a quiet home environment, it is difficult to attack in a cocktail-like environment, and it is difficult to improve on traditional acoustics.
Improvement of voice interaction capabilities
In terms of voice interaction, large models have a significant advantage in semantic expression. The parameters and volume of traditional speech recognition models are very limited, and programmes now based on large neural network models allow for multilingual joint modelling and multilingual identification. This makes humanoid robots easier to communicate with users from different linguistic backgrounds, expanding their geographical scope of application and user groups.
Large model improves the core capabilities of humanoid robots
Natural language processing and interaction
The AI mega-models perform well in the processing of natural languages (NLPs), enabling humanoid robots to understand and generate natural languages. By training on large-scale text data, robots can understand user instructions and requests more accurately and generate language-appropriate answers. For example, in the field of passenger service, human-shaped robots can provide detailed and accurate solutions based on user advice; in the field of education, they can interact with students to answer questions; In accompanying the scenes, emotional responses can also be given to users and more natural and fluid communication can be achieved.
Visual perception and processing
AI Big Models perform well in computer visual missions and can help humanoid robots to better understand and process visual information. Using deep learning models, humanoid robots can perform such functions as object identification, facial recognition, attitude estimation, and landscape understanding. In complex outdoor environments, autonomous navigation through attitude estimation and scenario understanding improves the efficiency and security of their work.
Action planning and control
Large AI models also have important applications for action planning and control. By enhancing learning and in-depth learning techniques, robots can learn complex actions and tasks, such as walking, moving objects, and assembly parts. These models can continuously adapt and optimize robotic behaviour based on real-time environmental feedback, making it more flexible and adaptable. In the case of humanoid robots on industrial production lines, it is able to adapt its actions and processes flexibly to the different specifications and production requirements of the product and to perform various assembly and processing tasks.
Multi-modal fusion
AI Big Models can handle multiple mosaics of data (e.g. text, images, audio, etc.), which are important for multimodular perception and decision-making by humanoid robots. Robots can use both visual and voice information to understand the user’s intentions and to respond more accurately and intelligently. For example, when a user issues a command, robots can understand the content of a command not only through voice recognition, but also by combining visual information to determine the environment and target location of the directive, thereby fulfilling the task more precisely. Multi-modular integration also enhances the situational perception of robots and their performance in complex scenarios.
Independent learning and adaptation
Large models are powerful for self-learning through extensive data training. Humanoid robots can use them for self-learning and improvement, gradually adapting to different environments and mission needs. For example, in different factory workshops, robots can learn to master new production processes and operating processes by learning themselves; in home service settings, as they interact with family members, they can understand the habits and needs of each member and provide more personalized services.
Status and results of the development of large smart models for humanoid robots
At present, many businesses and scientific institutions are actively involved in the development of large models of humanoid robotic intelligence, and have achieved some results. Science and technology for humanoid robotic technology was officially launched at the Global Development Day of Science and Technology in 2023, the "Big Model + Smart" event, and at the World Robotic Congress in 2024, a new and upgraded version of the Big Starfire Model + Smart Human Robot. Byte beats have also introduced a second-generation large model of robot GR-2 (Generative Robot 2.0), the state-of-the-art universal robotic agent who specializes in multifunctional and broad-based robotic operations.
At 2025 CES, the founder and CEO Wong In-hoon gave an opening speech and led 14 eco-humanoid robotic products to the spotlight. Among the 14 cooperative humanoid robot companies published by In Weida, Chinese manufacturers held six seats, including Wootree Technology, the cyborgs and Fouriers, and the peng cars, the Milky Way General, and the Star Activation Era. At the same time, Yin Weida announced the launch of the “World Foundation Model” NVIDIA Cosmos, which can be used by developers to create three-dimensional scenarios using Omniverse, which can then be converted to a photo-level reality scene, and to help robots find the best way to do their job by generating multiple models at the same time.
Challenges and prospects for the development of large smart models for humanoid robots
Although some progress has been made in the development of large smart models for humanoid robots, many challenges remain. First, at the technical level, the training of large models requires a great deal of computing resources and data and is costly, and the current model has some limitations in dealing with complex scenarios and uncertainties. Second, ethical and legal issues cannot be ignored. As humanoid robots become more intelligent, their behaviour and decision-making can have an impact on human society, and ethical norms and legal norms need to be put in place to regulate them. In addition, market acceptance is a problem, and users still have doubts about the safety, reliability and usefulness of humanoid robots.
Looking ahead, as technology progresses and improves, large models of humanoid robotic intelligence are expected to play an important role in more areas. In the field of industrial manufacturing, it can improve productivity and quality and achieve flexible manufacturing; in the field of medical care, it can assist doctors in operating and caring for patients; and in the area of family services, it can provide easier and more personalized life services. At the same time, with the depth of integration used in productive research, it is believed that more innovation will emerge, driving the development of large models of humanoid robotic intelligence to new steps.