Discovering Adaptable Symbolic Algorithms from Scratch
Autonomous robots deployed in the real world will need control policies that
rapidly adapt to environmental changes. To this end, we propose
AutoRobotics-Zero (ARZ), a method based on AutoML-Zero that discovers zero-shot
adaptable policies from scratch. In contrast to neural network adaption
policies, where only model parameters are optimized, ARZ can build control
algorithms with the full expressive power of a linear register machine. We
evolve modular policies that tune their model parameters and alter their
inference algorithm on-the-fly to adapt to sudden environmental changes. We
demonstrate our method on a realistic simulated quadruped robot, for which we
evolve safe control policies that avoid falling when individual limbs suddenly
break. This is a challenging task in which two popular neural network baselines
fail. Finally, we conduct a detailed analysis of our method on a novel and
challenging non-stationary control task dubbed Cataclysmic Cartpole. Results
confirm our findings that ARZ is significantly more robust to sudden
environmental changes and can build simple, interpretable control policies.
64c881d5d418013c77e86888
Stephen Kelly
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Daniel S. Park
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Xingyou Song
claimed_verified
2023-08-01T18:23:11.293Z
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Xingyou Song
user
Srizzle
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Mitchell McIntire
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Pranav Nashikkar
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Ritam Guha
admin_assigned
2023-08-01T10:18:03.522Z
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Ritam Guha
user
Ritam
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Wolfgang Banzhaf
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Kalyanmoy Deb
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Vishnu Naresh Boddeti
claimed_verified
2023-08-01T13:08:46.991Z
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/avatars/8335029e610788a7009e58024e77a4ac.svg
Vishnu Boddeti
user
vishnuboddeti
64c881d5d418013c77e86891
Jie Tan
64c881d5d418013c77e86892
Esteban Real
Discovering Adaptable Symbolic Algorithms from Scratch
Autonomous robots deployed in the real world will need control policies that
rapidly adapt to environmental changes. To this end, we propose
AutoRobotics-Zero (ARZ), a method based on AutoML-Zero that discovers zero-shot
adaptable policies from scratch. In contrast to neural network adaption
policies, where only model parameters are optimized, ARZ can build control
algorithms with the full expressive power of a linear register machine. We
evolve modular policies that tune their model parameters and alter their
inference algorithm on-the-fly to adapt to sudden environmental changes. We
demonstrate our method on a realistic simulated quadruped robot, for which we
evolve safe control policies that avoid falling when individual limbs suddenly
break. This is a challenging task in which two popular neural network baselines
fail. Finally, we conduct a detailed analysis of our method on a novel and
challenging non-stationary control task dubbed Cataclysmic Cartpole. Results
confirm our findings that ARZ is significantly more robust to sudden
environmental changes and can build simple, interpretable control policies.
AutoRobotics-Zero utilizes AutoML-Zero to develop adaptable control policies for robots that can modify their algorithms on-the-fly, surpassing neural network baselines in robustness to sudden environmental changes.
AutoML-Zero zero-shot adaptable policies linear register machine modular policies non-stationary control task Cataclysmic Cartpole