Qualcomm incorporated (20240119363). SYSTEM AND PROCESS FOR DECONFOUNDED IMITATION LEARNING simplified abstract
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 20240119363 titled 'SYSTEM AND PROCESS FOR DECONFOUNDED IMITATION LEARNING
Simplified Explanation
The abstract describes a patent application for a processor-implemented method that involves a robotic device observing an environment through sensors, generating beliefs about the environment based on past actions, and then controlling the robotic device to perform actions based on those beliefs.
- Observing environment via sensors
- Generating beliefs about the environment using an inference model
- Controlling robotic device based on generated beliefs
Potential Applications
This technology could be applied in various industries such as manufacturing, agriculture, healthcare, and security for tasks like automated assembly, crop monitoring, patient care, and surveillance.
Problems Solved
This technology helps in improving efficiency, accuracy, and autonomy in tasks performed by robotic devices by enabling them to make decisions based on their observations and past experiences.
Benefits
The benefits of this technology include increased productivity, reduced errors, cost savings, and improved safety in various applications where robotic devices are used.
Potential Commercial Applications
The potential commercial applications of this technology could include autonomous robots for warehouse management, smart farming solutions, robotic surgery systems, and security robots for patrolling and monitoring.
Possible Prior Art
One possible prior art for this technology could be the use of machine learning algorithms in robotics to improve decision-making processes based on data and observations.
Unanswered Questions
How does this technology handle unexpected changes in the environment?
This technology may need to incorporate adaptive algorithms or real-time learning capabilities to adjust to unexpected changes in the environment.
How does this technology ensure data privacy and security?
Data privacy and security measures such as encryption, access controls, and secure communication protocols may need to be implemented to protect the sensitive information collected and processed by the robotic device.
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.
