Ted Hirokawa
May 18, 2025 06:59
In the robot assembly, explore NVIDIA’s R² progress and utilize AI and machine learning to increase the improved adaptability and precision of contact with rich in contact.
NVIDIA Research has published significant developments in the field of robot assembly through robot research and developing Digest (R²D²). According to NVIDIA, this initiative focuses on the operation workflow, which is rich in contacts that solve the limitations of fixed automation and improve solidability, adaptability and scalability in dynamic environments.
Understanding manipulation rich in contact
Rich in contact includes a task of maintaining a continuous or repeated physical contact with an object, requiring accurate control of force and exercise. Such complex tasks are essential in industries such as robotics, manufacturing and automobiles, and precision is important for tasks such as peg insertion, mesh gear insertion and snap fitting parts assembly.
High -end workflow for robot assembly
NVIDIA’s research introduces some workflows that robots can deal with complex assembly work with improved flexibility. This includes:
- factory: Simulation tool kit for real -time contact interaction.
- Industry: Algorithms that robots can learn assembly work in simulation and apply in real scenarios.
- Automation: Framework for training robot assembly policies in various shapes.
- matchmaker: Pipeline for generating assembly asset pairs using the creation AI.
- SRSA: Framework for applying existing technology to new assembly work.
- TACSL: A library for simulating Visuotactile sensor data.
- paddle: Using force measurements to facilitate the transmission of the zero shot of the reinforcement learning policy.
Basic Technology: Factory, Industry and Automation
Factory Toolkit provides a physical -based simulation framework that enables real -time interaction modeling, while INDUSTREAL provides a high success rate with a success rate from the simulation of assembly technology to reality. By integrating reinforcement and imitation learning, we achieve zero shot simulation investment transmission to further automate these features.
New border exploration
NVIDIA continues to pursue the boundaries of robot assembly with advanced learning algorithms and automation technologies. Matchmaker automates asset production for various assembly tasks, SRSA improves technology search and adaptation, and TACSL accelerates Visuotactile sensor simulation to make tactile -based learning more practical.
Forging: Improvement of precision of manipulation
Forge introduces a zero-shot seesaw-truly a policy that uses power as a decisive input for tasks with high precise requirements. This innovation supports safe exploration and execution that shows the efficacy in complex assembly work under considerable uncertainty.
Visit the official blog for detailed insights in NVIDIA’s innovation in robot assembly.
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