title: Discovering Adaptable Symbolic Algorithms from Scratch
summary: 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.
paper_authors._id: 64c881d5d418013c77e86888
paper_authors.name: Stephen Kelly
paper_authors._id: 64c881d5d418013c77e86889
paper_authors.name: Daniel S. Park
paper_authors._id: 64c881d5d418013c77e8688a
paper_authors.name: Xingyou Song
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-08-01T18:23:11.293Z
paper_authors._id: 64c93c8c4524c2aea71d2ec6
paper_authors.avatarUrl: /avatars/1961fb6209182bf4954778dc3d97dde5.svg
paper_authors.fullname: Xingyou Song
paper_authors.type: user
paper_authors.user: Srizzle
paper_authors._id: 64c881d5d418013c77e8688b
paper_authors.name: Mitchell McIntire
paper_authors._id: 64c881d5d418013c77e8688c
paper_authors.name: Pranav Nashikkar
paper_authors._id: 64c881d5d418013c77e8688d
paper_authors.name: Ritam Guha
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-08-01T10:18:03.522Z
paper_authors._id: 630f83eb982455e61cca3a93
paper_authors.avatarUrl: /avatars/2bc517f35a23413be3a5d881ad5683eb.svg
paper_authors.fullname: Ritam Guha
paper_authors.type: user
paper_authors.user: Ritam
paper_authors._id: 64c881d5d418013c77e8688e
paper_authors.name: Wolfgang Banzhaf
paper_authors._id: 64c881d5d418013c77e8688f
paper_authors.name: Kalyanmoy Deb
paper_authors._id: 64c881d5d418013c77e86890
paper_authors.name: Vishnu Naresh Boddeti
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-08-01T13:08:46.991Z
paper_authors._id: 644c56f08c51ddbe0ea88f5c
paper_authors.avatarUrl: /avatars/8335029e610788a7009e58024e77a4ac.svg
paper_authors.fullname: Vishnu Boddeti
paper_authors.type: user
paper_authors.user: vishnuboddeti
paper_authors._id: 64c881d5d418013c77e86891
paper_authors.name: Jie Tan
paper_authors._id: 64c881d5d418013c77e86892
paper_authors.name: Esteban Real
paper_title: Discovering Adaptable Symbolic Algorithms from Scratch
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: AutoML-Zero zero-shot adaptable policies linear register machine modular policies non-stationary control task Cataclysmic Cartpole