18526443. DEEP REINFORCEMENT LEARNING FOR ROBOTIC MANIPULATION simplified abstract (GOOGLE LLC)
Contents
- 1 DEEP REINFORCEMENT LEARNING FOR ROBOTIC MANIPULATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DEEP REINFORCEMENT LEARNING FOR ROBOTIC MANIPULATION - 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
DEEP REINFORCEMENT LEARNING FOR ROBOTIC MANIPULATION
Organization Name
Inventor(s)
Sergey Levine of Berkeley CA (US)
Ethan Holly of San Francisco CA (US)
Shixiang Gu of Mountain View CA (US)
Timothy Lillicrap of London (GB)
DEEP REINFORCEMENT LEARNING FOR ROBOTIC MANIPULATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18526443 titled 'DEEP REINFORCEMENT LEARNING FOR ROBOTIC MANIPULATION
Simplified Explanation
Implementations utilize deep reinforcement learning to train a policy neural network that parameterizes a policy for determining a robotic action based on a current state. Some of those implementations collect experience data from multiple robots that operate simultaneously. Each robot generates instances of experience data during iterative performance of episodes that are each explorations of performing a task, and that are each guided based on the policy network and the current policy parameters for the policy network during the episode. The collected experience data is generated during the episodes and is used to train the policy network by iteratively updating policy parameters of the policy network based on a batch of collected experience data. Further, prior to performance of each of a plurality of episodes performed by the robots, the current updated policy parameters can be provided (or retrieved) for utilization in performance of the episode.
- Deep reinforcement learning used to train policy neural network for robotic actions
- Experience data collected from multiple robots operating simultaneously
- Policy network guides exploration of tasks during episodes
- Policy parameters updated based on collected experience data
- Updated policy parameters utilized in performance of episodes
Potential Applications
This technology can be applied in various fields such as autonomous robotics, industrial automation, and artificial intelligence research.
Problems Solved
This technology helps in improving the efficiency and effectiveness of robotic actions by training neural networks to make decisions based on current states and collected experience data.
Benefits
The benefits of this technology include enhanced robotic performance, increased productivity in automation processes, and advancements in artificial intelligence algorithms.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of autonomous robots for tasks such as warehouse management, manufacturing processes, and healthcare assistance.
Possible Prior Art
One possible prior art for this technology could be the use of reinforcement learning in training neural networks for decision-making in robotics and automation systems.
What are the specific tasks that the robots are trained to perform using this technology?
The specific tasks that the robots are trained to perform using this technology could include navigation, object manipulation, assembly tasks, and other actions that require decision-making based on current states.
How does the simultaneous operation of multiple robots contribute to the training of the policy neural network?
The simultaneous operation of multiple robots contributes to the training of the policy neural network by providing a diverse set of experience data that can be used to improve the performance and generalization of the policy network across different scenarios and environments.
Original Abstract Submitted
Implementations utilize deep reinforcement learning to train a policy neural network that parameterizes a policy for determining a robotic action based on a current state. Some of those implementations collect experience data from multiple robots that operate simultaneously. Each robot generates instances of experience data during iterative performance of episodes that are each explorations of performing a task, and that are each guided based on the policy network and the current policy parameters for the policy network during the episode. The collected experience data is generated during the episodes and is used to train the policy network by iteratively updating policy parameters of the policy network based on a batch of collected experience data. Further, prior to performance of each of a plurality of episodes performed by the robots, the current updated policy parameters can be provided (or retrieved) for utilization in performance of the episode.
