Robotic Action Policy Transfer Across Simulation and Real-World Inputs
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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 transfer learned policies from simulation to reality effectively, especially when dealing with changing network morphology or new input modalities.
Innovation Solution
A neural network system that includes a simulation-trained DNN with sufficient capacity and depth, and one or more robot-trained DNNs with minimal capacity, allowing for flexible task adaptation and efficient action selection by bridging the reality gap through progressive networks, enabling faster learning and reduced computational footprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep reinforcement learning algorithms are used to train policies 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 training process is segmented into two distinct phases: simulation training and real-robot training. The simulation-trained DNN handles the bulk of complex learning in a virtual environment, while the robot-trained DNN with minimal capacity performs only the final adaptation on the real robot. This segmentation allows most computationally intensive work to occur in simulation where resources are abundant, reducing the burden on real-robot computing resources.
Solution Approach 2:
A simulation environment acts as an intermediary between theoretical reinforcement learning algorithms and real-world robot execution. The simulation-trained DNN serves as a bridge, pre-processing complex learning tasks in a controlled virtual environment before transferring the learned policies to the actual robot, thereby accelerating real-world training while reducing computational requirements on the physical system.
2Adaptability or versatility
If a simulation-trained DNN with sufficient capacity and depth is used, then the network can learn tasks from scratch in simulation, but transferring to real robots requires accommodating dissimilar inputs and changing network morphology
Solution Approach 1:
The system dynamically adapts network capacity across different training phases. The simulation-trained DNN operates with full capacity and depth to learn complex tasks from scratch. When transferring to the real robot, the system switches to a robot-trained DNN with minimal capacity that is dynamically adjusted to match the specific requirements of the physical environment, allowing flexible accommodation of dissimilar inputs without maintaining fixed complex architecture.
Solution Approach 2:
Different parts of the neural network system have different capacities optimized for their specific functions. The simulation-trained DNN has sufficient capacity and depth for comprehensive task learning in simulation, while the robot-trained DNN has minimal capacity tailored specifically for real-robot adaptation. This local optimization allows each component to be precisely matched to its operational requirements rather than using a uniform architecture throughout.
3Adaptability or versatility
If existing approaches are used for policy transfer, then training can occur on real robots, but they are unable to accommodate changing network morphology or new input modalities
Solution Approach 1:
The system performs preliminary training in simulation where the agent learns tasks from scratch using a fully-capable DNN. This preliminary action in the virtual environment prepares the policy before real-robot training begins, allowing the actual robot training to focus only on adapting to physical realities rather than learning basic task concepts, thereby accommodating new input modalities without losing time on fundamental learning.
Data Source
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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.