Google llc (20240131695). DEEP REINFORCEMENT LEARNING FOR ROBOTIC MANIPULATION simplified abstract
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 20240131695 titled 'DEEP REINFORCEMENT LEARNING FOR ROBOTIC MANIPULATION
Simplified Explanation
Implementations of deep reinforcement learning in robotic action determination involve training a policy neural network to make decisions based on the current state of a robot.
- The policy neural network parameterizes a policy for determining robot actions.
- Experience data is collected from multiple robots operating simultaneously.
- Each robot generates experience data during iterative task performance episodes.
- The policy network guides the robots during task exploration episodes.
- Collected data is used to train the policy network by updating policy parameters.
- Updated policy parameters are utilized in subsequent task performance episodes.
Potential Applications
This technology can be applied in various fields such as autonomous robotics, industrial automation, and smart manufacturing processes.
Problems Solved
This innovation addresses the challenge of efficiently training robots to perform complex tasks in dynamic environments.
Benefits
- Improved efficiency and accuracy in robotic task performance. - Adaptability to changing environments and tasks. - Reduction in human intervention for robotic operations.
Potential Commercial Applications
"Enhancing Robotic Automation Efficiency with Deep Reinforcement Learning"
Possible Prior Art
Prior art in this field includes traditional robotic control methods, manual programming of robot actions, and basic reinforcement learning techniques.
Unanswered Questions
How does this technology handle unexpected obstacles or changes in the environment during task performance?
The abstract does not provide details on how the policy network adapts to unexpected situations or environmental changes.
What is the computational complexity of training the policy neural network with data from multiple robots?
The abstract does not mention the computational resources required for training the policy network with experience data from multiple robots.
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.
