#robots

You program the OpenMV Cam in Python. We make it easy to run machine vision algorithms and AI models on what the OpenMV Cam sees and then actuate hardware in the real world. Sense, plan, and act all in one Python script.

What makes microcontrollers unique is their low power consumption, low heat generation, small size, and ability to draw microwatts of power in deep sleep. This enables you to build tiny devices that can survive for years on batteries.

Beyond putting all these features into such a small footprint, we believe in giving you the tools to easily integrate the OpenMV Cam into any system. Each board exposes plenty of GPIO pins that provide SPI, I2C, I3C, UART, CAN, PWM, and ADC functionality.

For professionals, our schematics are available online so you can fully understand every OpenMV Cam and its accessories. You can modify and compile our firmware from GitHub, and SWD and JTAG are exposed for you to single step and debug your changes.

Robostral Navigate is an 8B model that enables robots to autonomously navigate complex environments using only a single RGB camera, achieving 76.6% success on unseen R2R-CE benchmarks—outperforming multi-sensor approaches while being more efficient. Built entirely in-house with simulated data and token-efficient techniques, it generalizes across robot types and adapts to real-world obstacles unseen during training. The model combines pointing-based navigation with reinforcement learning for continuous improvement, paving the way for unified embodied AI in robotics.

State-of-the-art performance on R2R-CE

79.4% Success Rate on validation seen

76.6% Success Rate on validation unseen 

Operates from a single RGB camera, with no LiDAR or depth sensors

8B model, built in-house and trained entirely in simulation

Runs on wheeled, legged, and flying robots, and generalizes across robot sizes

Robust to differences in camera intrinsics

Token-efficient training via prefix-caching

A key ingredient of Robostral Navigate is an efficient training algorithm based on prefix-caching. Using a tree-based attention-masking strategy, our method compresses an entire episode into a single sequence, enabling training on all time steps in a single forward pass while preventing information leakage between time steps.

Compared to training with one sample per time step, our approach reduces the number of training tokens by 22× while preserving all of the learning signals. In practice, this method transforms training runs that would take months into runs that complete in days.

Home chores robot.

A good video is at https://www.theguardian.com/science/2026/apr/22/ai-powered-robot-beats-elite-table-tennis-players-milestone-robotics

Here we present Ace, to our knowledge the first real-world autonomous system competitive with elite human table tennis players. Ace addresses the challenges of physical real-time interaction through a new, high-speed perception system using event-based vision sensors4, and a new control system based on model-free reinforcement learning, as well as state-of-the-art high-speed robot hardware. Evaluated in matches against elite and professional players under official competition rules, Ace achieved several victories and demonstrated consistent returns of high-speed, high-spin shots. These results highlight the potential of physical AI agents to perform complex, real-time interactive tasks, suggesting broader applications in domains requiring fast, precise human–robot interaction.

Ace is equipped with a new perception system using event-based vision sensors as well as a control system based on policies learnt using deep RL. In contrast to earlier approaches using RL for robot table tennis, the control policies used by Ace are learnt using an asymmetric actor–critic architecture27,28,29, and the actions produced by the policies exist in an abstract space that is then mapped to a hard constraint for a convex optimization problem. This setup allows learning of collision-free, agile motions, addressing the full challenge of human-competitive robot table tennis.

The perception system uses a combination of conventional APS cameras for ball triangulation and EVS cameras for ball angular velocity estimation to infer the current ball state at high frequencies. The ball state is then provided to two different control components, depending on whether Ace is serving or in a rally. When serving, the robot performs a single-arm serve from a library of serve motions that were found using a genetic algorithm.

During the rally, a fixed deep RL policy (π′) is queried at 31.25 Hz using the robot joint states and the ball position and spin histories. The policy is sampled during the match from a bank of policies trained to perform different skills.

The actions (a) produced by the policy are mapped to a 32-ms segment trajectory, and a corresponding reset trajectory is calculated. If the robot has yet to hit the ball and no collisions are predicted, then the segment trajectory is executed by the robot interface; otherwise, a reset trajectory is executed.

The training of all policies is performed entirely in simulation with custom physics models, noise models and data-driven distributions of the initial ball state. Training is performed asynchronously with multiple instances of the training environment. To aid in the learning process, the critic () is provided with the true ball state, whereas the policy (πi) is given a history of noisy sensor measurements.

On Sunday, Lightning, a 5-foot-5-inch, bright red humanoid robot, completed a half-marathon in record time, almost completely on its own. But about 220 yards from the finish line, Lightning slammed into a barricade and fell over, almost as if it was giving up.  Despite the tumble, Lightning got back up with the help of its team and completed the race in just under 51 minutes.  Even with the disruption, Lightning outperformed the current world record holder, Jacob Kiplimo, who completed the half-marathon in 57 minutes, 20 seconds.

China has a large lead in robot manufacturing, shipping more than 1,000 humanoid robots, according to The Journal. No American company has shipped more than 500.

Why is there such a push for humanoid robots? Several countries are betting high on humanoid robots, but no country is betting more than China, as Straight Arrow News has previously reported. It makes sense since China has the most to gain from the technology.  China has maintained its position as the world’s largest manufacturer, but a massive issue could erode its dominance. Following decades of its One-Child Policy, China faces population decline. The sentiment among Chinese people has also exacerbated the problem, as fewer people are less eager to work low-paying manual labor jobs.  Robots could eliminate that issue for China, creating an entire labor force using its massive manufacturing infrastructure. China also understands humanoid robots would benefit every country and being the first country to perfect the tech would give the country massive leverage over others.

Many U.S. tech companies understand that advanced humanoid robotics will likely be a massive future tech market but they are focusing more on artificial intelligence. Many of them believe that building more advanced AI systems first allows them to create any technology afterward, only much faster.