Robot Policy Learning From Experience Data and Predicted Rewards
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Solution Overview
Problem
Existing methods for training neural networks to control robots require extensive interaction with the environment and manually engineered rewards, which can cause wear and tear on the robot and are not robust for new tasks.
Innovation Solution
A system that utilizes robot experience data, including camera and sensor data accumulated during previous tasks, to train a policy neural network off-line using minimal demonstration data and a reward model to predict task-specific rewards, allowing the robot to perform new tasks without additional interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive interaction with the environment is used to train neural networks, then the robot learns to perform tasks, but wear and tear on the robot increases
Solution Approach 1:
The patent creates virtual copies of the physical environment through simulated experiences stored in never-ending storage. These virtual environments allow the robot to train extensively without physical wear, as the neural network learns from simulated rather than physical interactions. The copying principle is applied by generating synthetic training data that replicates real-world scenarios.
Solution Approach 2:
The system performs preliminary actions by pre-collecting and storing diverse environmental experiences in never-ending storage before they are needed for specific tasks. This pre-prepared database of experiences allows the robot to quickly adapt to new tasks without extensive real-time interaction, reducing wear during actual task execution.
2Reliability
If manually engineered rewards are used for training, then the robot can learn specific tasks, but the system complexity increases
Solution Approach 1:
The robot performs self-service by autonomously generating its own training experiences and learning from them without requiring manual reward engineering. The system collects experiences during normal operation and uses them to automatically improve task performance, eliminating the need for complex manually-designed reward functions while maintaining learning effectiveness.
3Adaptability or versatility
If additional robot interaction is required for new tasks, then the robot can adapt to new tasks, but wear and tear increases
Solution Approach 1:
The system performs preliminary action by pre-collecting a diverse database of environmental experiences in never-ending storage before new tasks are encountered. When a new task arises, the robot queries this pre-prepared database and learns from relevant past experiences without requiring extensive new physical interactions, thus adapting to new tasks while minimizing additional wear.
Solution Approach 2:
The never-ending storage system serves multiple functions: it stores experiences from various tasks, provides training data for new tasks, and enables transfer learning across different task domains. This universal experience repository allows the robot to adapt to any new task by drawing from its accumulated diverse experiences rather than requiring task-specific physical training.
4Productivity
If minimal demonstration data is used for training, then the training process is more efficient, but the robustness of the control policy decreases
Solution Approach 1:
The patent merges minimal demonstration data with extensive experiences from never-ending storage to create a comprehensive training dataset. This combination allows the system to benefit from both the efficiency of minimal demonstration data and the robustness provided by large volumes of diverse experiences, achieving high training efficiency while maintaining control policy robustness.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for data-driven robotic control. One of the methods includes maintaining robot experience data; obtaining annotation data; training, on the annotation data, a reward model; generating task-specific training data for the particular task, comprising, for each experience in a second subset of the experiences in the robot experience data: processing the observation in the experience using the trained reward model to generate a reward prediction, and associating the reward prediction with the experience; and training a policy neural network on the task-specific training data for the particular task, wherein the policy neural network is configured to receive a network input comprising an observation and to generate a policy output that defines a control policy for a robot performing the particular task.