18459258. SYSTEM AND PROCESS FOR DECONFOUNDED IMITATION LEARNING simplified abstract (QUALCOMM Incorporated)
Contents
- 1 SYSTEM AND PROCESS FOR DECONFOUNDED IMITATION LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEM AND PROCESS FOR DECONFOUNDED IMITATION LEARNING - 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 Original Abstract Submitted
SYSTEM AND PROCESS FOR DECONFOUNDED IMITATION LEARNING
Organization Name
Inventor(s)
Johann Hinrich Brehmer of Amsterdam (NL)
Hanno Ackermann of Amsterdam (NL)
Taco Sebastiaan Cohen of Amsterdam (NL)
Daniel Hendricus Franciscus Dijkman of Haarlem (NL)
SYSTEM AND PROCESS FOR DECONFOUNDED IMITATION LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18459258 titled 'SYSTEM AND PROCESS FOR DECONFOUNDED IMITATION LEARNING
Simplified Explanation
The abstract describes a processor-implemented method for a robotic device to observe an environment, generate a belief about the environment based on prior actions, and then perform actions in the environment based on this belief.
- Observing environment via sensors
- Generating belief using an inference model
- Controlling robotic device based on belief
Potential Applications
This technology could be applied in various industries such as:
- Autonomous vehicles
- Industrial automation
- Surveillance systems
Problems Solved
This technology helps in:
- Improving efficiency of robotic devices
- Enhancing decision-making capabilities
- Increasing safety in autonomous systems
Benefits
The benefits of this technology include:
- Increased accuracy in robotic operations
- Better adaptation to changing environments
- Enhanced overall performance of robotic devices
Potential Commercial Applications
The potential commercial applications of this technology could be seen in:
- Manufacturing
- Agriculture
- Logistics
Possible Prior Art
One possible prior art could be the use of machine learning algorithms in robotics to improve decision-making processes.
What are the specific types of sensors used in this technology?
The abstract does not specify the types of sensors used in this technology.
How does the inference model generate beliefs about the environment?
The abstract does not detail the specific methodology of how the inference model generates beliefs about the environment.
Original Abstract Submitted
A processor-implemented method includes observing an environment via one or more sensors associated with a robotic device. The processor-implemented method also includes generating, via an inference model, a belief of the environment based on data associated with prior actions of the robotic device in the environment. The processor-implemented method further includes controlling the robotic device to perform an action in the environment based on generating the belief.
