A robot hand built from Lego pieces by a 16-year-old and his father can grab and move objects, displaying similar qualities to a leading robotic hand. Jared Lepora, a student at Bristol Grammar School, UK, began developing the hand when he was 14 with his father, Nathan Lepora, who works at the University of Bristol. The device borrows principles from cutting-edge robotic hands, including the Pisa/IIT SoftHand, but uses only off-the-shelf parts from Lego Mindstorms, a line of educational kits for building programmable robots. "My dad's a professor at Bristol University for robotics, and I really liked the designs [of robotic hands]," says Jared. "It just inspired me to do it in an educational format and out of Lego." The hand is driven by two motors using tendons, and each of its four fingers has three joints.

Abstract-- This paper introduces an anthropomorphic robot hand built entirely using LEGO MINDSTORMS: the Educational SoftHand-A, a tendon-driven, highly-underactuated robot hand based on the Pisa/IIT SoftHand and related hands. T o be suitable for an educational context, the design is constrained to use only standard LEGO pieces with tests using common equipment available at home. The hand features dual motors driving an agonist/antagonist opposing pair of tendons on each finger, which are shown to result in reactive fine control. The finger motions are synchonized through soft synergies, implemented with a differential mechanism using clutch gears. Altogether, this design results in an anthropomorphic hand that can adaptively grasp a broad range of objects using a simple actuation and control mechanism. Since the hand can be constructed from LEGO pieces and uses state-of-the-art design concepts for robotic hands, it has the potential to educate and inspire children to learn about the frontiers of modern robotics.

Multi-step spatial reasoning entails understanding and reasoning about spatial relationships across multiple sequential steps, which is crucial for tackling complex real-world applications, such as robotic manipulation, autonomous navigation, and automated assembly. To assess how well current Multimodal Large Language Models (MLLMs) have acquired this fundamental capability, we introduce \textbf{LEGO-Puzzles}, a scalable benchmark designed to evaluate both \textbf{spatial understanding} and \textbf{sequential reasoning} in MLLMs through LEGO-based tasks. LEGO-Puzzles consists of 1,100 carefully curated visual question-answering (VQA) samples spanning 11 distinct tasks, ranging from basic spatial understanding to complex multi-step reasoning. Based on LEGO-Puzzles, we conduct a comprehensive evaluation of state-of-the-art MLLMs and uncover significant limitations in their spatial reasoning capabilities: even the most powerful MLLMs can answer only about half of the test cases, whereas human participants achieve over 90\% accuracy. In addition to VQA tasks, we evaluate MLLMs' abilities to generate LEGO images following assembly illustrations. Our experiments show that only Gemini-2.0-Flash and GPT-4o exhibit a limited ability to follow these instructions, while other MLLMs either replicate the input image or generate completely irrelevant outputs. Overall, LEGO-Puzzles exposes critical deficiencies in existing MLLMs' spatial understanding and sequential reasoning capabilities, and underscores the need for further advancements in multimodal spatial reasoning.

Visual perspective-taking (VPT), the ability to understand the viewpoint of another person, enables individuals to anticipate the actions of other people. For instance, a driver can avoid accidents by assessing what pedestrians see. Humans typically develop this skill in early childhood, but it remains unclear whether the recently emerging Vision Language Models (VLMs) possess such capability. Furthermore, as these models are increasingly deployed in the real world, understanding how they perform nuanced tasks like VPT becomes essential. In this paper, we introduce two manually curated datasets, Isle-Bricks and Isle-Dots for testing VPT skills, and we use it to evaluate 12 commonly used VLMs. Across all models, we observe a significant performance drop when perspective-taking is required. Additionally, we find performance in object detection tasks is poorly correlated with performance on VPT tasks, suggesting that the existing benchmarks might not be sufficient to understand this problem. The code and the dataset will be available at https://sites.google.com/view/perspective-taking

I have been working a lot with LEGO and 3D models lately. For my current project I am looking to build a LEGO image recognition program. My ideal scenario is to grab a handful of LEGO, toss them on the table, take a picture, and have the program catalog the pieces. The biggest challenge I encounter with any machine learning project is collecting and formatting the training data. I am pretty sure this is the biggest challenge everyone encounters with machine learning.

The next time you clean up your LEGO collection, you're going to wish you had this machine at home to do the dirty work. Built pretty much entirely with LEGO pieces (plus a Raspberry Pi and some motors), this thing is able to sort virtually any LEGO piece that comes down its conveyer belts, thanks to artificial intelligence. It uses a neural network--or a set of algorithms that recognize patterns, similar to the human brain--to match the real-world LEGO pieces with 3D images of the pieces that the machine has been fed during training. The machine, which was built by YouTuber Daniel West, isn't the first of its kind--though it looks to be the most effective. There are plenty of other contraptions on YouTube showing crazy machines that others have built to sort LEGO bricks, like machines that sort LEGO axles, specifically, and others that spin plastic cups around to catch parts.

The Danish toy company has launched a global trial with four building sets and hopes to release more next year depending on user feedback. Using the design script used by Lego creators, known as LXFML data, the Austrian Research Institute for Artificial Intelligence developed software that is able to turn the instructions into braille and English audio. The idea came about after blind entrepreneur and Lego enthusiast Matthew Shifrin previously relied on a friend to translate instructions into braille, which enabled him to build independently for the first time. After his friend died, Mr Shifrin was introduced to Lego's Creative Play Lab and decided to push for development of the technology: "This is extremely important for blind children because there aren't a lot of places where we can say'Look Mum and Dad! I built this on my own... I did this'," he said.

Artificial Intelligence is like Lego; to build something nice, you need to combine the right pieces in the right way. Most of us have played with Lego when we were small. I did at least and I absolutely loved it. I can remember the days when my friends and I were playing with Lego for hours on end, constantly creating new structures and building complete cities. We loved it and I am sure it stimulated my creativity. Even adults still play with Lego, often in group exercises to get some creativity flowing.

David Aguilar has built himself a robotic prosthetic arm using Lego pieces after being born without a right forearm due to a rare genetic condition. Aguilar, 19, who studies bioengineering at the Universitat Internacional de Catalunya in Spain, is already using his fourth model of the colorful prosthetic and his dream is to design affordable robotic limbs for those who need them. Once his favorite toys, the plastic bricks became the building material for Aguilar's first, still very rudimentary, artificial arm at the age of nine, and each new version had more movement capability than the one before. David Aguilar has built himself a robotic prosthetic arm using Lego pieces after being born without a right forearm due to a rare genetic condition. 'As a child I was very nervous to be in front of other guys, because I was different, but that didn't stop me believing in my dreams,' Aguilar, who is from Andorra, a tiny principality between Spain and France, told Reuters.

Artificial Intelligence is like Lego; to build something nice, you need to combine the right pieces in the right way. Most of us have played with Lego when we were small. I did at least and I absolutely loved it. I can remember the days when my friends and I were playing with Lego for hours on end, constantly creating new structures and building complete cities. We loved it and I am sure it stimulated my creativity. Even adults still play with Lego, often in group exercises to get some creativity flowing.