Addverb Technologies, backed by the Reliance Group, is rapidly catching up with the global robotics industry. The company recently announced that it will launch its first humanoid robots in 2025, with an initial plan to roll out 100 units. Sangeet Kumar, the co-founder and CEO of Addverb, stated in an interview that these robots will be utilized across various sectors including energy and retail. Addverb has a strong background.

The rapid development of deep learning relies on the scale of datasets, models, and computational power. In the fields of natural language processing and computer vision, researchers have already discovered a power-law relationship between model performance and data scale. However, in the field of robotics, especially in robot manipulation, such scalable patterns have yet to be established. A research team from Tsinghua University recently published a paper exploring data scaling laws in robot imitation learning and proposed an efficient data collection strategy that collected sufficient data in just one afternoon, enabling...

At an auction in New York, a painting created by a humanoid robot was successfully sold for $1.08 million (approximately £566,000, AU$1,630,000), making it the first piece of robot art to be sold at auction. The painting, titled "A.I. God. Portrait of Alan Turing," depicts the famous mathematician Alan Turing and stands 2.2 meters (7.5 feet) tall, exceeding pre-auction estimates of $120,000 to $180,000.

This week, MIT showcased a new model for training robots, aimed at addressing the challenges faced by imitation learning when introduced to small obstacles. Researchers pointed out that imitation learning can fail in conditions such as varying lighting, different environments, or new obstacles because robots simply do not have enough data to adapt. The team sought powerful data methods, like those used in models such as GPT-4, to tackle the problem. They introduced a new architecture called the Heterogeneous Pretrained Transformer (HPT), which aggregates information from different sensors and environments.

Kind Humanoid is a robotics startup based in Palo Alto, founded by Christoph Kostol, who has a background in Silicon Valley. The company's workspace is filled with various electronic components and fabrication tools, reflecting the spirit of DIY computing that has characterized Silicon Valley. The design of Kind Humanoid's robots is inspired by the surrealistic art style of Belgian painter René Magritte. The company's designer, Yves Béhar, stated that he hopes to convey a message through the design of the robots.

According to Qunzhi Consulting, the overall demand for robots globally shows a continuous growth trend, with an expected shipment scale of approximately 47 million units in 2024, maintaining a compound annual growth rate of over 20% for the next five years. Furthermore, the revenue scale is expected to reach nearly $128 billion by 2029. Image source note: Image generated by AI, with image licensing provided by Midjourney. In terms of shipment volume, Qunzhi Consulting's data indicates that in 2023, consumer robots accounted for about 81% of the global market shipments, maintaining a long-term dominant position in the robot market.

Robot startup 1X Technologies has developed a new generative model that enhances the efficiency of training robotic systems in simulations. The company announced this model in a new blog post, addressing one of the significant challenges in robotics, which is learning the 'World Model (1X World Model)' that predicts how the world responds to the actions of robots. When robots start from the same sequence of initial images, this world model can envision multiple possibilities based on different action proposals.

Norwegian startup 1X Technologies recently announced significant advancements in developing AI-based world models for robots. Simply put, these models act as virtual testing grounds for robots, allowing them to test and improve in various scenarios without the need for real-world testing. 1X believes this is key to solving the 'robotics challenge' — how to reliably evaluate robots trained for multiple tasks in ever-changing environments.

Recently, Amazon began testing advertisements in its newly launched shopping chatbot, Rufus. This information was revealed through an update log released by Amazon. The company stated that in the future, users of Rufus will see some sponsored ads related to their searches during conversations. These ads will not only appear in the chat interface, but may also be accompanied by text generated by Rufus to help users better understand the ad content.

The technology company 'Accelerated Evolution', dedicated to humanoid robot research and development, has completed hundreds of millions in pre-A funding, with investors including Bianshi Times, Minyin International, Zhongguancun Science City, and iCANX Fund, along with follow-up investment from Inno Angel. The funds will primarily be used for the development of a general ontology and enhancement of software development capabilities. CEO Cheng Hao pointed out that the AI-driven humanoid robot boom, especially the demand for vehicles of Artificial General Intelligence (AGI), is driving the transition of humanoid robots from specialization to generalization. The company's phase one commercial positioning is to provide developers with general biped robots.

Noted robotics expert David Hanson has joined WorkFar Robotics as Chief Executive Advisor, focusing on innovations in human-robot collaboration technology. WorkFar is dedicated to providing intelligent humanoid robot solutions for the warehousing and manufacturing industries, aimed at addressing labor shortages. Hanson, along with his team, developed the 'Syntro' synthetic robot, which combines artificial intelligence with remote human control technology to ensure safe operation in high-risk tasks. Additionally, Hanson plans to send his created robot 'Sophia' to Mars.

The computer science research team at the University of Idaho's Coeur d’Alene campus has developed an autonomous robot that uses artificial intelligence for precision weeding, capable of identifying and targeting half-inch wide weeds through electric shock eradication. After demonstration at the Coeur d’Alene nursery conducted by the Forestry Bureau, plans are underway to improve the hardware and electrical components to enhance the AI's accuracy in weed detection. This project, in collaboration with the USDA, aims to increase reforestation efficiency in the national forest system, reduce labor costs and pesticide use, and bolster local ecosystems.

NAVIAI 2.0, independently developed by the Zhejiang Humanoid Robot Innovation Center, is a humanoid robot that stands 1.65 meters tall, weighs 60 kilograms, boasts 41 degrees of freedom, and has an AI computing power of 275 Tops. It showcases exceptional performance and a highly anthropomorphic design, with the ability to execute smooth and natural movements. It can perform complex tasks such as giving speeches, making tea, and playing chess, demonstrating a very high level of anthropomorphic motion mapping execution ability. At the exhibition, it not only served as the host but also displayed its ability to quickly learn new movements and perform high-precision tasks in industrial settings.

Tesla CEO Elon Musk has raised the idea that human memories and cognitive states could be uploaded to the cloud and downloaded into humanoid robots, attracting widespread attention. He envisions the Tesla Optimus robot not only performing dangerous and repetitive tasks but also becoming a companion, caring for children, and even participating in sports events. Musk predicts that there will be 10 billion robots globally, with a market size of $25 trillion, far exceeding the electric vehicle market. Tesla plans to begin production of the Optimus robot next year, with expected pricing.

To test the 'stress resistance' of these AIs, a group of researchers embarked on an 'adventure'. They created a test called Self-Contradictory Instructions (SCI), which is essentially the 'death challenge' for AI. This test includes 20,000 self-contradictory instructions, covering both language and visual domains. For example, you might be shown a picture of a cat and asked to describe this 'dog'. Isn't that a challenge for humans? Oh no, it's a challenge for AI.