Robot Policy Networks Bridging Simulation and Real-World Learning
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Solution Overview
Problem
Deep reinforcement learning algorithms are too slow to achieve performance on real-world robots and require significant computing resources, and existing approaches struggle to accommodate changing network morphology or new input modalities, limiting their effectiveness in bridging the reality gap between simulated and real-world environments.
Innovation Solution
A neural network system comprising a simulation-trained DNN and one or more robot-trained DNNs, where the simulation-trained DNN has sufficient capacity and depth to learn tasks from scratch, and the robot-trained DNNs have minimal capacity, allowing for faster learning and flexibility in adapting to real-world environments with varying input modalities, while maintaining a smaller computational footprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep reinforcement learning algorithms are used to train robotic agents on real-world robots, then the agent can learn complex tasks, but the training process is too slow and requires significant computing resources
Solution Approach 1:
The patent applies preliminary action by training a simulation-trained DNN in a virtual environment before deploying it to the real robot. The simulation-trained DNN learns complex tasks in advance through deep reinforcement learning, achieving high-level task understanding without requiring slow real-world trial and error. This pre-trained network then serves as a foundation for the robot-trained DNN, which only needs to adapt to real-world nuances, dramatically accelerating the training process on actual robots.
Solution Approach 2:
The patent introduces an intermediary approach by using a progressive neural network architecture that bridges simulation and reality. The simulation-trained DNN acts as an intermediary model that captures general task logic, while the robot-trained DNN serves as another intermediary layer that adapts this knowledge to the specific real robot. This progressive training pipeline mediates between the computational efficiency of simulation and the real-world applicability required for actual deployment.
2Adaptability or versatility
If existing approaches are used to bridge the reality gap between simulation and real-world environments, then some transfer learning can be achieved, but the approaches struggle to accommodate changing network morphology or new input modalities
Solution Approach 1:
The patent applies dynamics by implementing a progressive neural network architecture where the network morphology can dynamically evolve. The system starts with a simulation-trained DNN with a specific architecture, then progressively develops robot-trained DNNs that can modify their structure to accommodate new input modalities and robot-specific characteristics. This dynamic adaptation allows the network to change its morphology while maintaining the core knowledge learned in simulation, thereby accommodating new sensors, actuators, or environmental conditions without retraining from scratch.
3Reliability
If a simulation-trained DNN with sufficient capacity and depth is used to learn tasks from scratch, then comprehensive task understanding is achieved, but the computational footprint increases
Solution Approach 1:
The patent applies segmentation by dividing the neural network training process into distinct phases and components. The simulation-trained DNN is trained separately in simulation with sufficient capacity to learn complex tasks, while the robot-trained DNN is trained separately on the real robot with focus on adaptation. This segmentation allows each network to be optimized for its specific purpose, reducing the overall computational footprint compared to training one monolithic network for both simulation and reality.
Solution Approach 2:
The patent uses copying by creating a robot-trained DNN that copies and adapts the knowledge structure from the simulation-trained DNN. Rather than requiring the full computational capacity of a simulation-trained network for real-world operation, the system copies the essential task understanding and adapts it to the specific robot, maintaining performance while reducing computational requirements for deployment.
Data Source
AI summary
A system includes a neural network system implemented by one or more computers. The neural network system is configured to receive an observation characterizing a current state of a real-world environment being interacted with by a robotic agent to perform a robotic task and to process the observation to generate a policy output that defines an action to be performed by the robotic agent in response to the observation. The neural network system includes: (i) a sequence of deep neural networks (DNNs), in which the sequence of DNNs includes a simulation-trained DNN that has been trained on interactions of a simulated version of the robotic agent with a simulated version of the real-world environment to perform a simulated version of the robotic task, and (ii) a first robot-trained DNN that is configured to receive the observation and to process the observation to generate the policy output.