Labelvisor

As robotic systems evolve from executing simple commands to complex, multi-step operations, the way data is structured and annotated must also change. Modern robots are expected to perform long sequences of actions in dynamic environments or assist humans in performing real-world tasks. All of these capabilities depend on a new class of data - long-horizon task data.<...

Recognition and annotation of transparent and reflective objects is one of the most challenging tasks in robotic perception. Despite significant progress in computer vision and deep learning, current systems still face fundamental limitations when working with materials that violate standard assumptions about diffuse light reflection. Glass, mirror surfaces, and other semi-tr...

The manipulation of textile products belongs to the most complex challenges of modern robotics due to the fundamental difference between rigid objects and deformable materials. While in classic industrial automation, a robot inte...

Now that robotics is moving from the controlled factory environment to the real world, we face the challenge of giving machines the ability to use tools they've never seen before. We can take a new kitchen utensil or a workshop tool and quickly figure out how to use it based on form, function, and context. But for robots, this ability is not innate; it has to be learned throu...

Modern robotics is moving towards tasks that require interaction with delicate and deformable objects, such as food products, medical materials, and fragile household items. In these conditions, traditional rigid grippers are ineffective due to the risk of damaging objects and their limited adaptability to their shapes.

Soft robotic grippers offer a fundamentally differ...