China's Unitree soars in market debut as investors bet on humanoid robots China's Unitree Robotics has soared in its market debut, a sign of investors' confidence in Chinese innovation in the burgeoning field of human-like robots. Shares of Unitree surged more than 620 percent on Wednesday as it began trading in Shanghai, lifting the Hangzhou-based company's market capitalisation to as high as 445 billion yuan ($66bn). Unitree, whose main competitors include China's AgiBot and US-based Tesla and Boston Dynamics, had priced its initial public offering at 50.8 yuan per share for a company valuation of about 61 billion yuan ($9bn). The landmark IPO marked the first listing of a humanoid robotics company in mainland China. Unitree, founded in 2016 by engineer Wang Xingxing, is one of China's leading makers of humanoid robots, with its demonstrations of human-like figures performing dancing and martial arts routines capturing widespread attention.

This year, RoboCup took place in Incheon, South Korea, from 2-6 July. The event saw teams take part in competitions, training sessions, and a symposium. Take a look at what the participants got up to in our round up from social media. RoboCup 2026 officially begins today! A post shared by RoboCup Federation (@robocup.official)

Undergrads' weed-killing robot wins top prize A team of Cornell undergraduates beat 95 other teams to take the grand prize at The Farm Robotics Challenge with their invention: an autonomous robot that kills weeds with electricity. Their robot can travel through a vineyard or orchard without a human operator, zapping weeds with a small amount of electricity, saving labor and energy and preventing crop loss, without the use of herbicides. Led by Andrew James, an agricultural sciences major in the College of Agriculture and Life Sciences (CALS), the team of agricultural specialists and engineers studied the existing electrical weeding technology, developed their own low-energy system and built a working prototype over the course of four intense months. Natalia Kurz, a biological engineering major in CALS, said the project required a lot of late nights. "There were fears for us, like, was it just going to be for nothing?"

This year, RoboCup took place in Incheon, South Korea, from 2-6 July. The event saw teams take part in competitions, training sessions, and a symposium. Take a look at what the participants got up to in our round up from social media. RoboCup 2026 officially begins today! A post shared by RoboCup Federation (@robocup.official)

NVIDIA introduces Halos for Robotics, which the company calls the industry's first full-stack safety system for robotics and physical AI operating near people.

The dust at this busy recycling plant is pervasive and the steady noise of hoppers and conveyor belts makes this a challenging environment to work in. The facility in Rainham, east London is owned by Sharp Group, a family-run skip and waste management firm. Along the conveyor belts runs everything you could imagine, from shoes, to old VHS cassettes and blocks of concrete. The team here processes up to 280,000 tonnes of mixed recycling every year with 24 agency workers on its rapid conveyor belts. This is a hazardous industry.

Humanoid home robots are on the market - but do we really want them? Last year, Norwegian-US tech company 1X announced a strange new product: "the world's first consumer-ready humanoid robot designed to transform life at home". Standing 168 centimetres tall and weighing in at 30 kilograms, the US$20,000 Neo bot promises to automate common household chores such as folding laundry and loading the dishwasher. Neo has a built-in artificial intelligence (AI) system, but for tricky tasks it requires a 1X employee wearing a virtual reality helmet to remotely take over the robot. The operator can see whatever the bot does inside your house, and the process is recorded for future learning.

Applications of Reinforcement Learning (RL) in robotics are often limited by high data demand. On the other hand, approximate models are readily available in many robotics scenarios, making model-based approaches like planning a data-efficient alternative. Still, the performance of these methods suffers if the model is imprecise or wrong. In this sense, the respective strengths and weaknesses of RL and model-based planners are complementary. In the present work, we investigate how both approaches can be integrated into one framework that combines their strengths. We introduce Learning to Execute (L2E), which leverages information contained in approximate plans to learn universal policies that are conditioned on plans. In our robotic manipulation experiments, L2E exhibits increased performance when compared to pure RL, pure planning, or baseline methods combining learning and planning.

Data-driven learning of soft continuum robot (SCR) dynamics from high-dimensional observations offers flexibility but often lacks physical interpretability, while model-based approaches require prior knowledge and can be computationally expensive. We bridge this gap by introducing (1) the Attention Broadcast Decoder (ABCD), a plug-and-play module for autoencoder-based latent dynamics learning that generates pixel-accurate attention maps localizing each latent dimension's contribution while filtering static backgrounds. (2) By coupling these attention maps to 2D oscillator networks, we enable direct on-image visualization of learned dynamics (masses, stiffness, and forces) without prior knowledge. We validate our approach on single- and double-segment SCRs, demonstrating that ABCD-based models significantly improve multi-step prediction accuracy: 5.7x error reduction for Koopman operators and 3.5x for oscillator networks on the two-segment robot. The learned oscillator network autonomously discovers a chain structure of oscillators. Unlike standard methods, ABCD models enable smooth latent space extrapolation beyond training data. This fully data-driven approach yields compact, physically interpretable models suitable for control applications.

Current upper limb prostheses aim to enhance user independence in daily activities by incorporating basic motor functions. However, they fall short of replicating the natural movement and interaction capabilities of the human arm. In contrast, human limbs leverage intrinsic compliance and actively modulate joint stiffness, enabling adaptive responses to varying tasks, impact absorption, and efficient energy transfer during dynamic actions. Inspired by this adaptability, we developed a transhumeral prosthesis with Variable Stiffness Actuators (VSAs) to replicate the controllable compliance found in biological joints. The proposed prosthesis features a modular design, allowing customization for different residual limb shapes and accommodating a range of independent control signals derived from users' biological cues. Integrated elastic elements passively support more natural movements, facilitate safe interactions with the environment, and adapt to diverse task requirements. This paper presents a comprehensive overview of the platform and its functionalities, highlighting its potential applications in the field of prosthetics.