18125503. CONTROLLING A ROBOT DURING INTERACTION WITH A HUMAN simplified abstract (NVIDIA Corporation)
Contents
- 1 CONTROLLING A ROBOT DURING INTERACTION WITH A HUMAN
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CONTROLLING A ROBOT DURING INTERACTION WITH A HUMAN - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
CONTROLLING A ROBOT DURING INTERACTION WITH A HUMAN
Organization Name
Inventor(s)
Sammy Joe Christen of Luzern (CH)
Wei Yang of Lake Forest Park WA (US)
Claudia Perez D'arpino of Seattle WA (US)
Yu-Wei Chao of Redmond WA (US)
CONTROLLING A ROBOT DURING INTERACTION WITH A HUMAN - A simplified explanation of the abstract
This abstract first appeared for US patent application 18125503 titled 'CONTROLLING A ROBOT DURING INTERACTION WITH A HUMAN
Simplified Explanation
The patent application describes apparatuses, systems, and techniques for controlling a real-world and/or virtual device, such as a robot, using neural networks. The neural networks are trained to control the movement of agents towards specific targets while avoiding collisions with obstacles.
- Neural networks are used to control the movement of a device, such as a robot.
- The neural networks are trained to guide agents towards targets while avoiding collisions with obstacles.
- Parameter values for the neural networks are updated through training with different scenarios involving multiple agents and targets.
Potential Applications
The technology could be applied in various fields such as robotics, autonomous vehicles, and virtual reality simulations.
Problems Solved
This technology helps in efficiently controlling devices to navigate complex environments and avoid collisions.
Benefits
The use of neural networks allows for adaptive and intelligent control of devices in dynamic environments.
Potential Commercial Applications
The technology could be utilized in industries such as manufacturing, logistics, and entertainment for automated control systems.
Possible Prior Art
Prior art may include patents related to neural network-based control systems for robots or autonomous vehicles.
Unanswered Questions
How does the training process for the neural networks work in practice?
The article does not provide details on the specific training algorithms or methodologies used to train the neural networks for controlling the devices.
What are the limitations of using neural networks for device control?
The article does not discuss any potential drawbacks or challenges associated with implementing neural network-based control systems for real-world applications.
Original Abstract Submitted
Apparatuses, systems, and techniques to control a real-world and/or virtual device (e.g., a robot). In at least one embodiment, the device is controlled based, at least in part on, for example, one or more neural networks. Parameter values for the neural network(s) may be obtained by training the neural network(s) to control movement of a first agent with respect to at least one first target while avoiding collision with at least one stationary first holder of the at least one first target, and updating the parameter values by training the neural network(s) to control movement of a second agent with respect to at least one second target while avoiding collision with at least one non-stationary second holder of the at least one second target.
