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As the neural network gets better at the task and reaches a performance threshold, the amount of domain randomization is increased automatically. This makes the task harder, since the neural network must now learn to generalize to more randomized environments.
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The network keeps learning until it again exceeds the performance threshold, when more randomization kicks in, and the process is repeated. We apply the same technique to all other parameters, such as the mass of the cube, the friction of the robot fingers, and the visual surface materials of the hand.
Domain randomization required us to manually specify randomization ranges, which is difficult since too much randomization makes learning difficult but too little randomization hinders transfer to the real robot. ADR solves this by automatically expanding randomization ranges over time with no human intervention. ADR removes the need for domain knowledge and makes it simpler to apply our methods to new tasks.
In contrast to manual domain randomization, ADR also keeps the task always challenging with training never converging. We compared ADR to manual domain randomization on the block flipping task, where we already had a strong baseline. In the beginning ADR performs worse in terms of number of successes on the real robot.
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But as ADR increases the entropy, which is a measure of the complexity of the environment, the transfer performance eventually doubles over the baseline—without human tuning. This is because ADR exposes the network to an endless variety of randomized simulations.
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It is this exposure to complexity during training that prepares the network to transfer from simulation to the real world since it has to learn to quickly identify and adjust to whatever physical world it is confronted with. We find that our system trained with ADR is surprisingly robust to perturbations even though we never trained with them: The robot can successfully perform most flips and face rotations under all tested perturbations, though not at peak performance. We believe that meta-learning , or learning to learn, is an important prerequisite for building general-purpose systems, since it enables them to quickly adapt to changing conditions in their environments.
The hypothesis behind ADR is that a memory-augmented networks combined with a sufficiently randomized environment leads to emergent meta-learning , where the network implements a learning algorithm that allows itself to rapidly adapt its behavior to the environment it is deployed in. We perform these experiments in simulation, which allows us to average performance over 10, trials in a controlled setting.
In the beginning, as the neural network successfully achieves more flips, each successive time to success decreases because the network learns to adapt. When perturbations are applied vertical gray lines in the above chart , we see a spike in time to success.
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This is because the strategy the network is employing doesn't work in the changed environment. The network then relearns about the new environment and we again see time to success decrease to the previous baseline. We also measure failure probability and performed the same experiments for face rotations rotating the top face 90 degrees clockwise or counterclockwise and find the same pattern of adaptation.
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