The question of whether a robot for parcel sorting needs two legs and ten fingers remains open. X Square Robot placed a two-armed robot in front of a conveyor belt, equipped with standard industrial grippers. This robot worked with real logistics parcels that varied in size, weight, material, and orientation. During one hour of live broadcast, it performed 1816 effective sorting operations, which is 45% higher than the Figure 03's rate of 1248 parcels per hour.
Both companies engage in similar activities, but their hardware philosophies differ significantly. Figure 03 uses a full-scale humanoid body and five-fingered hands. X Square Robot, however, has abandoned legs and extra fingers, retaining only two manipulators and grippers. As a result, the solution costs approximately 70% less, while maintaining a sorting accuracy exceeding 98%. The parcels processed were not uniform: they included cardboard boxes, soft bags, cylindrical shipments, and fresh goods in styrofoam packaging, simulating a real sorting line.
The key element is the model behind the grippers. In April 2026, X Square Robot introduced WALL-B. This model is built on a single unified architecture that simultaneously trains vision, language, action, and prediction of physical changes within one network, instead of piecing together VLA components. The model determines the next step and predicts the behavior of surrounding objects. Simply seeing a gripper close on a soft bag is not enough; the system must anticipate whether the bag will slip through the middle. When moving a box sideways, it is necessary to assess whether it will shift, rotate, or tip over. This prediction is then used to adjust actions: either by lifting with two arms or by using inertia and the work surface to flip items.
This approach, similar to the idea of DeepSeek, is applied to the hardware: the model takes on the complexity, allowing the body to remain simple and inexpensive. The grippers sacrifice degrees of freedom, and WALL-B compensates for them with smarter strategies dependent on the parcel's state. Hardware still matters: gripper opening, friction, arm load, and force accuracy determine the robot's capabilities. However, now the same standard equipment can handle arbitrary scenes. Figure maintains a fully humanoid form for mobility, tool use, and fine manipulation, whereas X Square Robot supports a minimalist body for the conveyor station, asserting that warehouses should not pay for walking, balance, and ten fingers.
The same model is applicable to both home and industry. After releasing the first comprehensive base model, WALL-A, at the end of 2024, X Square Robot began deploying robots in nursing homes and regular residential homes. In 2026, the company partnered with 58 Daojia to provide on-demand intelligent cleaning services, becoming the first company to implement embodied AI in ordinary homes at scale. WALL-B was tested in these fragmented environments before moving to warehouse settings. Economic factors are what the industry calls the 'killer line': efficiency, cost, stability, and reproduction speed. As models begin to understand weight, friction, and inertia, robots can use fewer expensive bodies, and embodied intelligence approaches overcoming this barrier.
Beijing-based embodied-AI startup X Square Robot has made HOST open-source. This framework enables a humanoid robot to observe a brief demonstration video and successfully replicate the task on its initial try. Using just a 29-second clip, HOST achieved a 62 percent task-success rate, significantly surpassing the industry standard, which typically demands around 50 expert demonstrations and four hours of fine-tuning to reach only 38 percent success.
The prevailing method in embodied-AI involves collecting teleoperation data. In this process, an engineer uses a VR headset to manually guide a robot arm through a task frame by frame, repeating the sequence numerous times before running supervised fine-tuning overnight. The expense associated with this method is substantial; a single data-collection robot can cost hundreds of thousands of dollars, and the marginal cost for one demonstration can exceed one hundred dollars.
While data factories in India and part-time workers in Chinese county towns offer the most affordable source of motion data, the overall cost structure remains difficult to scale.
HOST fundamentally reverses this established procedure. Rather than instructing the robot to mimic human joint movements, HOST prompts the robot to conceptualize the final state and subsequently derive the necessary motion. The framework incorporates two core components: a vision module, which processes the human demonstration to render the scene as the robot perceives it, and an action module, which calculates the joint trajectory required to produce that specific rendering.
Furthermore, a dynamic time-warping alignment module is integrated to map the frames from the human video onto the robot's execution steps, reducing alignment errors by approximately an order of magnitude. This methodology draws inspiration from cognitive science theories of simulation, which posit that humans acquire new motor skills by mentally rehearsing the intended result prior to performing the action.
The Chinese team adapted this cognitive logic for robotic learning, replacing traditional offline fine-tuning with inference-time in-context learning. In this setup, the robot does not undergo retraining on new data; instead, it treats the provided video as a prompt. X Square Robot was established in late 2023 and has since secured over one billion yuan across eight funding rounds, with investors including Meituan Strategic Investment, Alibaba Cloud, Lenovo Capital, and Delian Capital.
The decision to release HOST as open-source represents a strategic move aimed at gaining developer attention as a pathway toward enterprise pilot programs. Although the 62 percent figure serves as a research benchmark and not a ready-to-deploy consumer robot, it effectively redefines the discussion regarding the future architecture of embodied-AI training pipelines once inference-time learning becomes the standard practice.
As part of the upcoming Amazon Great Freedom Festival Sale, starting on August 7th, Dreame Technology has announced price reductions on several of its products in the electronics and home appliance categories. The company is offering discounts on robot vacuum cleaners, stick vacuums, wet and dry vacuums, and hair dryers.
During this promotion, buyers can take advantage of bank offers, cashback, and an EMI option without extra charges. However, the final cost may vary depending on the specific bank and the terms of the offer.
The most significant price reduction is observed on the Dreame D9 Max model, whose price has dropped from 29,999 rupees to 14,999 rupees. The Dreame D10 Plus Gen 2 model is now available for 23,499 rupees, down from its original price of 39,999 rupees. Additionally, the L10 Prime has been reduced from 45,999 rupees to 34,999 rupees.
In the premium segment, the Dreame L50s Pro Ultra price has been lowered from 79,999 rupees to 77,999 rupees. The L50 Ultra CE model is also available for 59,999 rupees, while the D30 Ultra is priced at 49,999 rupees and the D20 Ultra at 44,999 rupees.
Discounts are not limited to robot vacuum cleaners; the company is also offering special conditions on stick and wet-dry vacuums. For instance, the R10 Pro is now 15,999 rupees instead of 25,999 rupees, and the R20 is available for 24,999 rupees. Discounts have also been provided for the H12 Core and H12 Dual models.
If considering a hair dryer purchase, the Gleam Hair Dryer and Glory Hair Dryer from the company will be available at a starting price of 5,999 rupees during the sale.
Dreame Technology entered the Indian market at the end of 2023. The company states it is expanding its presence in categories such as robot vacuum cleaners, stick vacuums, wet-dry vacuums, and grooming products. According to the company, Dreame's share in the Indian robot vacuum cleaner market is showing rapid growth.
Experts advise potential buyers not to make decisions solely based on discounts. Before purchasing smart home appliances, it is recommended to compare prices across various e-commerce platforms and study previous price trends, as the final price may change after accounting for bank offers and coupons.
