Robot Action Policy Transfer Using Simulation-Trained Neural Networks
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Solution Overview
Problem
Deep reinforcement learning algorithms are too slow to achieve performance on real-world robots and struggle to effectively transfer learned policies from simulated environments to real-world scenarios, requiring more computing resources and being inflexible with changing network morphology or new input modalities.
Innovation Solution
A neural network system comprising a sequence of deep neural networks, including a simulation-trained DNN and one or more robot-trained DNNs, which allows for efficient policy learning on real robots by bridging the reality gap, enabling faster learning and reduced computational resources through differing capacities and architectures, and accommodating new input modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep reinforcement learning algorithms are used to train robotic agents directly on real-world robots, then the agent can learn from real environmental interactions, but the training process becomes extremely slow and computationally expensive
Solution Approach 1:
The patent applies preliminary action by first training the robotic agent in a simulated environment before deploying it to the real world. The simulation-trained policy serves as a pre-trained foundation that is then fine-tuned on the real robot, avoiding the need to train from scratch on the slow real-world hardware. This preliminary training in simulation accelerates the overall learning process while maintaining eventual real-world performance.
Solution Approach 2:
The patent introduces a simulation environment as an intermediary between the training process and the real-world robot. This intermediary allows most of the training to occur in the fast simulation, with only policy fine-tuning happening on the real robot. The intermediary simulation bridges the gap between rapid prototyping and real-world deployment, solving the speed-effectiveness contradiction.
2Productivity
If a simulation-trained policy is transferred directly to a real robot, then training speed improves, but the policy fails to perform effectively due to the reality gap
Solution Approach 1:
The patent implements feedback by using real-world interaction data to fine-tune the simulation-trained policy. The agent collects actual observations and rewards from the real robot, then uses this feedback to adjust and improve the policy. This closed-loop feedback process bridges the reality gap by adapting the simulated policy to real-world nuances while maintaining the speed benefits of simulation training.
Solution Approach 2:
The patent applies parameter changes by adapting the policy parameters based on real-world performance data. The simulation-trained policy parameters are fine-tuned using actual real-environment observations and rewards, adjusting the parameters to account for differences between simulation and reality. This parameter adaptation resolves the performance issue while preserving training efficiency.
3Productivity
If the neural network architecture is fixed for simulation training, then the model can be trained efficiently, but it cannot adapt to new input modalities or changing task requirements
Solution Approach 1:
The patent applies dynamics by making the neural network architecture adaptable and modifiable after initial training. The system allows the network structure to evolve dynamically as new tasks or input modalities are introduced. This dynamic architecture enables the model to be reconfigured for different tasks while maintaining the efficiency benefits of the originally trained structure.
Solution Approach 2:
The patent implements universality by designing a neural network architecture that can serve multiple functions and adapt to different tasks. The network is structured to accommodate various input modalities and task requirements, allowing the same base model to be applied across diverse robotic tasks. This multi-functional design maintains training efficiency while providing versatility.
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.