Systematic self-exploration of behaviors for robots in a dynamical systems framework

2018

Conference Paper

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One of the challenges of this century is to understand the neural mechanisms behind cognitive control and learning. Recent investigations propose biologically plausible synaptic mechanisms for self-organizing controllers, in the spirit of Hebbian learning. In particular, differential extrinsic plasticity (DEP) [Der and Martius, PNAS 2015], has proven to enable embodied agents to self-organize their individual sensorimotor development, and generate highly coordinated behaviors during their interaction with the environment. These behaviors are attractors of a dynamical system. In this paper, we use the DEP rule to generate attractors and we combine it with a “repelling potential” which allows the system to actively explore all its attractor behaviors in a systematic way. With a view to a self-determined exploration of goal-free behaviors, our framework enables switching between different motion patterns in an autonomous and sequential fashion. Our algorithm is able to recover all the attractor behaviors in a toy system and it is also effective in two simulated environments. A spherical robot discovers all its major rolling modes and a hexapod robot learns to locomote in 50 different ways in 30min.

@inproceedings{PinneriMartius2018:Repeller,
  title = {Systematic self-exploration of behaviors for robots in a dynamical systems framework},
  author = {Pinneri, Cristina and Martius, Georg},
  booktitle = {Proc.\ Artificial Life XI},
  pages = {319-326},
  publisher = {MIT Press, Cambridge, MA},
  year = {2018},
  doi = {10.1162/isal_a_00062},
  url = {https://www.mitpressjournals.org/doi/abs/10.1162/isal_a_00062}
}