Nvidia corporation (20240123620). GRASP POSE PREDICTION simplified abstract
Contents
- 1 GRASP POSE PREDICTION
GRASP POSE PREDICTION
Organization Name
Inventor(s)
Jonathan Tremblay of Redmond WA (US)
Stanley Thomas Birchfield of Sammamish WA (US)
Valts Blukis of Seattle WA (US)
GRASP POSE PREDICTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240123620 titled 'GRASP POSE PREDICTION
Simplified Explanation
The patent application focuses on apparatuses, systems, and techniques for generating and selecting grasp proposals using neural networks based on a latent code corresponding to an object.
- Neural networks are utilized to generate and select grasp proposals.
- The grasp proposals are based on a latent code that represents the object.
- The technology aims to improve the efficiency and accuracy of selecting grasps for objects.
Potential Applications
This technology could be applied in various industries such as robotics, manufacturing, and logistics for automating tasks that involve grasping objects.
Problems Solved
1. Streamlining the process of selecting appropriate grasps for different objects. 2. Enhancing the precision and reliability of robotic grasping tasks.
Benefits
1. Increased efficiency in handling objects. 2. Reduction in errors and damages during grasping operations. 3. Potential for improved productivity and cost savings in industries utilizing robotic systems.
Potential Commercial Applications
Optimizing Robotic Grasping Techniques for Enhanced Efficiency
Possible Prior Art
There may be existing patents or research papers related to robotic grasping techniques using neural networks and latent codes for object recognition and manipulation.
Unanswered Questions
How does this technology compare to traditional methods of grasp proposal generation and selection in terms of accuracy and efficiency?
The article does not provide a direct comparison between this technology and traditional methods of grasp proposal generation and selection.
What are the potential limitations or challenges in implementing this technology in real-world applications?
The article does not address any potential limitations or challenges that may arise in the practical implementation of this technology.
Original Abstract Submitted
apparatuses, systems, and techniques to generate and select grasp proposals. in at least one embodiment, grasp proposals are generated and selected using one or more neural networks, based on, for example, a latent code corresponding to an object.
