Reinforcement learning models trained only in simulation using automatic domain randomization solve Rubik's cube with a real robot hand.
Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience
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abstract
We consider the problem of transferring policies to the real world by training on a distribution of simulated scenarios. Rather than manually tuning the randomization of simulations, we adapt the simulation parameter distribution using a few real world roll-outs interleaved with policy training. In doing so, we are able to change the distribution of simulations to improve the policy transfer by matching the policy behavior in simulation and the real world. We show that policies trained with our method are able to reliably transfer to different robots in two real world tasks: swing-peg-in-hole and opening a cabinet drawer. The video of our experiments can be found at https://sites.google.com/view/simopt
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2019 2roles
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Sequential VAE embeds simulated trajectories into latent paths for Bayesian optimization with dynamic compression to enable data-efficient high-dimensional controller tuning on robots.
citing papers explorer
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Solving Rubik's Cube with a Robot Hand
Reinforcement learning models trained only in simulation using automatic domain randomization solve Rubik's cube with a real robot hand.
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Bayesian Optimization in Variational Latent Spaces with Dynamic Compression
Sequential VAE embeds simulated trajectories into latent paths for Bayesian optimization with dynamic compression to enable data-efficient high-dimensional controller tuning on robots.