Consumers who are often exposed to smart homes and whole-house smart devices can easily find that the current smart home industry has achieved widespread device coverage and control access, but the vast majority of AIoT devices are still in the passive execution stage of instructions, and real operational decisions still require user initiation.
Automated functions such as turning on lights by voice, controlling air conditioners via APP, and automatically drawing curtains when you return home have long become standard smart features for the whole house. However, the system itself does not have the ability to actively perceive the home scene and cannot independently provide services based on the real-time status of the home. This pain point has also become the core bottleneck of smart upgrades for the whole house.
In response to this industry pain point, Xiaopai Technology recently officially released the ClawStation series of home intelligent terminals, as well as the dedicated home care terminal “KinSight”, marking that whole-house intelligence has officially entered the AI Agent development stage.
Unlike traditional smart home hubs, which are only responsible for device connection and command forwarding, ClawStation Pro is equipped with a side AI computing module and supports four camera video inputs. It can rely on the built-in local model to conduct real-time analysis of home space images, identify the current status and needs of the home, and then use open interfaces to call smart home devices of different brands to perform corresponding operations, forming a complete closed loop of “perception-decision-execution”.
Traditional infrared sensors and millimeter-wave radars can only recognize basic information such as human presence and movement status, and cannot detect complex scenes such as paper scraps, water stains, and scattered objects on the ground. ClawStation Pro relies on its visual perception capabilities to accurately identify such subdivided scenes and make independent decisions to call equipment such as sweeping robots to complete cleaning tasks.
In addition, visual perception can also cover the needs of more home scenes: judging children’s learning status, identifying whether they watch TV at close range for a long time, assisting in checking the cleaning effect of housekeeping services, monitoring the health-related status of the elderly such as falling, sitting for a long time, and eating, upgrading the perception dimension of whole-house intelligence from “is there or not” to “who is, what is doing, and whether help is needed.”
The core advantage of ClawStation Pro is not only its perception ability, but also its continuous “evolution” attribute.
Different from the traditional smart hardware factory-fixed model, ClawStation Pro adopts a modular design with replaceable computing power cards. The current 30TOPS computing power card can meet the basic video processing needs. In the future, it can be adapted to more complex multi-modal models by replacing higher-specification computing power cards to avoid equipment obsolescence caused by hardware iteration.
At the same time, the device also supports non-sensory learning capabilities, which can gradually adapt to the house structure, member composition, and living habits of different families in daily use, continuously optimizing the accuracy of scene recognition and decision-making, and truly realizing an intelligent experience that “gets better with more use.”
Relevant technical leaders stated that visual perception is not intended to replace traditional sensors, but as a supplement to the existing perception system, providing higher-dimensional perception capabilities in complex scenes that traditional sensors cannot cover. Infrared, door sensors, temperature and humidity sensors, millimeter-wave radar and other equipment will still undertake basic sensing tasks with the advantages of low cost and low privacy risk.
In response to the privacy issue that users are most concerned about, ClawStation Pro adopts a local model architecture. The entire process of visual recognition, scene analysis, and decision generation are completed on local devices. There is no need to upload the complete video stream to the cloud. It only uploads desensitized non-video information when complex scenes require enhanced judgment, striking a balance between intelligent experience and privacy protection.
Regarding the development prospects of the home intelligent industry, the industry predicts that the next three years will usher in an inflection point for large-scale implementation: on the one hand, the maturity of AI Agent technology allows large model capabilities to be truly implemented in specific scene tasks, and is no longer limited to basic functions such as chatting and content generation; on the other hand, the effects of small parameter multi-modal models continue to improve, and the cost of side computing power continues to decrease, so that the threshold for local operation of complex AI capabilities continues to decrease.
In the future, with the continuous optimization of experience and further reduction of costs, home intelligence will gradually replace the traditional smart home hub, realizing a fundamental upgrade of whole-house intelligence from “passively responding to user instructions” to “actively providing scene services”.
