Robotics Research and Development Digest (R²D²)

Traditional task and motion planning (TAMP) systems for robot manipulation use cases operate on static models that often fail in new environments. Integrating...

While today's robots excel in controlled settings, they still struggle with the unpredictability, dexterity, and nuanced interactions required for real-world...

As physical AI systems advance, the demand for richly labeled datasets is accelerating beyond what we can manually capture in the real world. World foundation...

A major challenge in robotics is training robots to perform new tasks without the massive effort of collecting and labeling datasets for every new task and...

Robots must perceive and interpret their 3D environments to act safely and effectively. This is especially critical for tasks such as autonomous navigation,...

This edition of NVIDIA Robotics Research and Development Digest (R2D2) explores several contact-rich manipulation workflows for robotic assembly tasks from...

Robotic arms are used today for assembly, packaging, inspection, and many more applications. However, they are still preprogrammed to perform specific and often...

Welcome to the first edition of the NVIDIA Robotics Research and Development Digest (R2D2). This technical blog series will give developers and researchers...