Building realistic 3D environments for robotics simulation has traditionally been a labor-intensive process, often requiring weeks of manual modeling and setup....

Building realistic 3D environments for robotics simulation has traditionally been a labor-intensive process, often requiring weeks of manual modeling and setup. Now, with generative world models, you can go from a text prompt to a photorealistic, simulation-ready world in a fraction of time. By combining NVIDIA Isaac Sim, an open source robotics reference framework, with generative models such as…

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Robot manipulation systems struggle with changing objects, lighting, and contact dynamics when they move into dynamic real-world environments. On top of this,...

Robot manipulation systems struggle with changing objects, lighting, and contact dynamics when they move into dynamic real-world environments. On top of this, gaps between simulation and reality, and non-optimized grippers or tools often limit how reliably robots can generalize, execute long-horizon tasks, and achieve human-level dexterity across diverse tasks. This edition of NVIDIA Robotics…

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

Traditional task and motion planning (TAMP) systems for robot manipulation use cases operate on static models that often fail in new environments. Integrating perception with manipulation is a solution to this challenge, enabling robots to update plans mid-execution and adapt to dynamic scenarios. In this edition of the NVIDIA Robotics Research and Development Digest (R²D²)…

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