Robots today are capable of performing precise, repetitive tasks, but they often lack the flexibility to adapt to new situations or handle uncertainty. However, this is changing rapidly as robots become more intelligent. The question arises: what key technologies enable robotic intelligence? How have these technologies evolved over the past decade, and what are their future prospects?
With the growing demand for home robots and continuous advancements in artificial intelligence and hardware performance, service robots are transitioning from laboratories into homes. They are evolving from single-function devices like vacuum cleaners to multi-functional personal assistants.
**Perception, Cognition, and Behavior Control**
Robot technology typically breaks down into three main components: perception, cognition, and behavior control. Perception involves processing visual, auditory, and sensor data. Cognition handles higher-level tasks such as reasoning, planning, memory, and learning. Behavior control manages the robot's physical actions.
Artificial intelligence plays a central role in robotics. AI aims to replicate human-like intelligence through computers, and robots represent one of its ultimate applications. All three components—perception, cognition, and behavior control—are deeply connected to AI.
From an application perspective, robots must be autonomous and capable of interacting with people and their environment. Compared to traditional computing devices, robots require higher levels of intelligence, which has gradually drawn attention from AI research. This is one of the reasons why AI is increasingly being integrated into robotic systems.
Traditional AI originated in the 1950s at the Dartmouth Conference. Over time, it has developed rich theoretical foundations and practical methods—from symbolic computing systems and expert systems to machine learning and big data analysis. These have led to applied research in image recognition, speech processing, search engines, data mining, and social computing. Technologies like computer vision, speech recognition, and agents are closely tied to robotics.
While achieving human-level intelligence remains a distant goal, current technological progress allows for partial simulation of human-like behaviors, such as identifying users and responding accordingly. This can significantly improve user experience. However, challenges remain in ensuring the practicality and robustness of these technologies. Many robots are still limited to lab environments or controlled settings like nursing homes. Emerging home service robots, however, must operate independently or semi-independently, requiring stronger technical reliability.
**Robots Are More Than Just Machines and Chips**
In past research, housework was a common target for service robots, requiring them to manipulate objects using arms. Despite significant studies, achieving this within a few years remains challenging due to several factors.
First, current robots, especially humanoid ones, are expensive. A single robotic arm can cost tens of thousands of dollars, making them unaffordable for most households. Second, robotic dexterity still lags behind human hands. Third, safety concerns arise when robots with metal bodies interact with humans—errors could lead to serious harm.
If robots cannot perform housework, what is their purpose? This is a valid concern. If not widely applicable, robots risk becoming just machines and chips. However, beyond single-function robots like vacuum cleaners, many other types are under development.
As smart devices, robots are expected to offer unique value. While full housework remains a long-term goal, future service robots may focus on low cost, multifunctionality, and collaboration with humans. They will also prioritize security, both digital and physical, to ensure safe interaction with users.
**Future Applications of Home and Professional Service Robots**
Home service robots will bring various helper applications. For example, virtual assistants like Siri or Cortana could evolve into physical robots that interact naturally with users, offering personalized services such as reminding them of appointments or providing weather updates.
They can also assist children’s education by offering interactive learning experiences. In elderly care, robots can remind seniors to take medication or provide emergency assistance, improving quality of life and easing family burdens.
Professional service robots will find diverse applications, from logistics and retail to photography and security. For instance, Amazon already uses robots for cargo distribution, and some retailers use them to greet customers and assist with shopping.
**Key Technologies and Challenges**
Despite exciting future applications, many technical challenges remain. Three core areas—perception, cognition, and behavior control—are critical. Among them, behavior control has seen more development, while perception and cognition require further refinement.
Key technologies include 3D navigation, visual perception, language interaction, text recognition, and cognitive functions like planning and learning. Although progress has been made, many issues still need solving.
**R&D and Practical Application**
Intel China Research Institute focuses on service robots, working on robust navigation, visual perception, and human-robot interaction. Their research includes developing prototypes like the tablet robot shown in Figure 3, which integrates Intel Realsense cameras and SLAM technology for accurate navigation.
Current projects show promising results, such as long-term user tracking and environment mapping. Future developments aim to enhance these technologies and support industry partners in creating advanced service robots.
**Hardware and AI Integration**
The question of whether robots need dedicated AI chips remains open. Hybrid computing, combining general-purpose processors and specialized units, is a promising approach. While still in early stages, it offers flexibility and efficiency.
Today’s general-purpose processors and GPUs provide a solid foundation for service robots, especially those with perception and cognitive capabilities. As applications evolve, hardware design will face new challenges, and innovations like FPGAs may play a role in accelerating specialized tasks.
Overall, the integration of AI, hardware, and practical applications is shaping the future of service robots, making them more intelligent, affordable, and useful in everyday life.
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