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ETH Zurich researchers adapted a commercial robotic hand to crawl using its existing fingers, without adding wheels or legs.

The 818 g setup has 20 powered joints and carries its own battery, computer, and motion sensors. When one finger lifts to take a step or press a key, the others have to support the hand’s weight. The controls were trained in simulation to account for the unequal finger lengths and offset thumb.

They tested it on 14 surfaces, including grass, gravel, carpet, and metal grating. It recovered from falls in 21 of 25 trials and correctly pressed requested keyboard keys in 29 of 32 attempts without visual feedback. A separate object-pushing demonstration uses an overhead camera.

More details: https://www.automate.org/robotics/industry-insights/what-if-thing-from-addams-family-was-actually-a-robot

Kodiak says IKEA will be the launch shipper for its driverless long-haul service, planned to begin by the end of 2026. The driverless portion would cover 219 miles of I-45 between the Dallas-Fort Worth and Houston areas, within a longer 292-mile delivery route.

The companies have spent four years operating with a safety observer aboard, completing more than 1,300 IKEA loads and logging over 750,000 autonomous miles. The observer has not been removed yet.

Princeton researchers have tested PACMAN (Prediction And Control using MAchiNe learning), a framework that combines multiple machine learning models in a real-time control loop, at the DIII-D tokamak in San Diego.

The framework uses live plasma measurements to generate predictions and adjust heating and other reactor systems in milliseconds. Tests included predicting tearing mode instabilities 200 milliseconds before onset, detecting and controlling waves caused by fast particles, and coordinating all six of DIII-D’s gyrotrons.

PACMAN also resolves conflicting commands from different controllers and applies hardware safety limits before sending instructions to the tokamak. Human operators set the experimental objectives and control parameters.

Intrinsic has released Core, a free open-source toolkit aimed at making it easier to build robot applications on top of ROS.

The toolkit is hardware agnostic and includes pose estimation using NVIDIA FoundationPose, motion and grasp planning, Gazebo simulation, camera calibration and ROS drivers.

Intrinsic describes the components as building blocks that can be combined into larger robot behaviors rather than requiring developers to build everything from scratch.

Core is available under an Apache 2.0 license on GitHub.

Detroit is still building out its role as a major robotics and automation hub.

FANUC is investing $90 million in a new 840,000-square-foot Michigan facility that’s expected to add 225 jobs, while Teradyne Robotics is opening a Wixom operations hub that will manufacture Universal Robots cobots and could eventually include MiR mobile robots.

That’s on top of the existing robotics presence around Auburn Hills, Novi, Michigan Central/Newlab in Corktown, and the University of Michigan’s robotics program.

The industry itself is changing too. Non-automotive companies accounted for 55.5% of North American robot orders in 2025, compared with 31% in 2005, so robotics in the region is becoming a lot broader than traditional auto manufacturing.

Amazon is investing more than $100 million in a new robotics manufacturing facility in Greenwood, Indiana.

The 585,000-square-foot site is expected to open in 2028 and create about 300 manufacturing and engineering jobs. It will produce equipment for Amazon’s fulfillment and robotics network across North America.

Amazon also announced a robotics manufacturing facility in Austin in August and already operates robotics manufacturing sites in Massachusetts.

The company says it has now deployed more than one million robots across more than 300 facilities worldwide.

This article looks at the two main ways machines understand depth.

One approach uses LiDAR, radar, stereo, time-of-flight and other 3D sensors to measure distance directly. The other uses standard 2D cameras and neural networks to estimate 3D structure.

Caterpillar is used as an example of direct sensing and sensor redundancy in autonomous mining, while Tesla represents the camera-based inference approach.

The article breaks down where each method makes sense based on environment, cost, reliability and the consequences of getting depth wrong.

Robot dot com, formerly Kiwibot, is developing a new four-legged delivery robot called R-dog.

Each leg has a wheel, and the robot is designed to handle terrain that smaller sidewalk delivery bots struggle with, including stairs. The company says the first deployments are planned for college campuses in 2027.

They also announced a partnership with FieldAI to use its world models for autonomy and navigation.

CEO Felipe Chavez Cortes said the company considered humanoids for this kind of work, but doesn’t think they’re ready yet for outdoor delivery environments.

Niantic spent years building systems that could understand depth, location, streets, buildings, and other physical spaces for augmented reality.

That technology now sits at the center of Niantic Spatial, a spinoff building large geospatial models for robotics and physical AI.

The company has access to more than 30 billion posed images from millions of locations and is now working with Rancho Cordova, California, on a digital twin covering roughly four square miles of the city.

The model is already being used for traffic planning, with the broader goal of creating realistic environments where robots can train and be tested before operating in the real world.

Niantic Spatial CEO Inhi Cho Suh put the challenge pretty well: “Hallucination is a luxury that embodied AI can't afford.”

Robust AI has deployed its 100th Carter robot and named former Fox Robotics CEO Marin Tchakarov as its new CEO.

CTO Rodney Brooks says a robot only counts as deployed when customers rely on it in daily operations and the company has the support infrastructure to keep it running. He also discusses what separates working deployments from “humanoid deployment theater” and why scaling from 100 robots to 1,000 requires more service and support staff.

KAIST’s HOUND quadruped uses reinforcement learning to decide how to move based on the terrain in front of it.

Instead of relying on separate control programs for walking, running and jumping, the robot learned multiple movement skills under one framework and can switch between them as conditions change.

Researchers tested it across stairs, slopes, gaps, grass, forest trails and uneven terrain, with HOUND adapting its gait without human input.

Event-based sensors only generate data when a meaningful change occurs, rather than continuously streaming measurements.

In industrial settings, that can be useful for high-speed production, predictive maintenance and mobile robotics where latency, bandwidth and power consumption matter. They can also capture very short events that traditional periodically sampled sensors may miss.

The tradeoff is integration. Existing PLCs and legacy controls may not be designed to react to sensor data arriving at microsecond-level resolution, so edge computing or IPCs can be needed in between.

In most cases, the approach appears more complementary than replacement, with event-based sensors added alongside traditional cameras, proximity sensors and other existing systems.

As vehicles take over more of the driving, it becomes harder to determine who is responsible when something goes wrong.

Some systems can handle much of the driving but still require a person behind the wheel to stay alert and take over when needed. That can mean asking someone who has been passively monitoring the road for a long period to suddenly react to an emergency.

Remote operators create another question. If one person is monitoring several vehicles at once, how many can they realistically supervise before that becomes a safety issue?

Responsibility can fall differently on the driver, vehicle owner, manufacturer or remote operator depending on how automated the vehicle is and where it is operating.

Humanoid pilots are starting to focus on narrower tasks and simpler deployment models.

Toyota Research Institute is testing a progression from vision systems to specialized mobile manipulation before moving toward more complex humanoid systems. Other companies are also focusing on specific tasks, operational KPIs and collecting real-world data through deployment.