17972854. PROPRIOCEPTIVE LEARNING simplified abstract (Honda Motor Co., Ltd.)
Contents
- 1 PROPRIOCEPTIVE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PROPRIOCEPTIVE LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Proprioceptive Learning
- 1.13 Original Abstract Submitted
PROPRIOCEPTIVE LEARNING
Organization Name
Inventor(s)
Alireza Rezazadeh of Minneapolis MN (US)
Nawid Jamali of Dublin CA (US)
Soshi Iba of Mountain View CA (US)
PROPRIOCEPTIVE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 17972854 titled 'PROPRIOCEPTIVE LEARNING
Simplified Explanation
A robot for proprioceptive learning uses sensors, memory, and a processor to update graph representations based on sensor data and perform tasks.
- Sensors, memory, and a processor are used in the robot.
- The processor receives sensor reading and position data.
- Graph representations are constructed and updated based on the data.
- Tasks are executed using the updated graph representations.
Key Features and Innovation
- Utilizes sensors, memory, and a processor for proprioceptive learning.
- Constructs and updates graph representations based on sensor data.
- Executes tasks using the updated graph representations.
Potential Applications
The technology can be applied in robotics, artificial intelligence, and machine learning for tasks requiring proprioceptive learning capabilities.
Problems Solved
This technology addresses the need for robots to learn and adapt based on sensor data for improved performance in various tasks.
Benefits
- Enhanced learning and adaptation capabilities for robots.
- Improved performance in tasks requiring proprioceptive learning.
- Efficient utilization of sensor data for task execution.
Commercial Applications
- Robotics industry for advanced automation.
- Artificial intelligence applications for enhanced learning algorithms.
- Machine learning systems for improved task performance.
Prior Art
Further research can be conducted on prior art related to graph representations in robotics and machine learning for proprioceptive learning.
Frequently Updated Research
Stay updated on advancements in sensor technology, memory systems, and processor capabilities for enhanced proprioceptive learning in robots.
Questions about Proprioceptive Learning
How does proprioceptive learning benefit robotics technology?
Proprioceptive learning allows robots to adapt and improve performance based on sensor data, enhancing their capabilities in various tasks.
What are the potential limitations of using graph representations in proprioceptive learning for robots?
The limitations may include computational complexity, data processing requirements, and the need for efficient algorithms to update the graph representations effectively.
Original Abstract Submitted
According to one aspect, a robot for proprioceptive learning may include a set of sensors, a memory, and a processor. The processor may perform receiving a set of sensor reading data from the set of sensors, receiving a set of sensor position data associated with the set of sensors, constructing a first graph representation based on the set of sensor reading data, constructing a second graph representation based on the set of sensor position data, performing message passing operation between nodes of the first graph representation and the second graph representation to update the first graph representation and the second graph representation, and executing a task based on readouts from the updated first graph representation and the updated second graph representation.
