Rhoda AI (original) (raw)

Redefining Robotic Intelligence

We make robots smarter

We explore a new paradigm for scaling robot intelligence with web-scale video pre-training. We’ve published an in-depth research blog to discuss the novel architectural designs and the benefits of such an approach.

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Returns processing

This is an end-to-end returns processing task for a customer in the logistics industry. The task contains ambiguity—visually similar states can correspond to very different points in the pipeline. Rather than relying on the current frame alone the model maintains memory, in the form of a long history of frames.

Bearing decanting

This task comes from an automotive assembly line. The customer initially believed it could not be automated because of several challenges: each box weighs 10 kg, the lifting strap can easily tear, removing the tab requires precise control, and the transparent plastic bag is difficult for a robot to grasp.

Contico breakdown

Contico are 50-pound, heavy-duty boxes that are ubiquitous in manufacturing for transporting materials between facilities. After use, they must be manually cleared of debris of random sizes and type, unlatched, and collapsed for return or storage. This task is difficult to automate due to the large box size and variability in debris.

Human demo following

Long-context enables in-context learning, allowing us to inject human demonstrations into our robot's context window. This enables our robot to perform tasks single-shot, without retraining. Here we demonstrate single-shot pick and place and single-shot drawing from human demonstrations.

We work with a variety of customers across verticals in automotive, manufacturing, logistics, and ecommerce. If you’re interested in working with us at your facility, reach out here.