Hybrid system identification can be particularly challenging, particularly in the context of visuomotor control. We introduce switching density networks (SDNs), which can be used to identify switching control systems in an end-to-end learning fashion from demonstration data.
We show that SDNs, when paired with a general purpose family of proportional-integral-derivative control laws, can identify the pump, spin and balance controllers required to keep an inverted pendulum upright (see header). We also use them to identify the joint angle goals that make up an inspection task on a PR2 robot, and those needed to open a suitcase.
Switching density networks are particularly useful for options learning, as the controllers identified using these can be re-used elsewhere. Importantly, by embedding structure into the network, SDNs become more interpretable, and allow for hierarchical learning that is not possible with their closely related counterparts, mixture density networks.
M Burke, Y Hristov, S Ramamoorthy, Switching Density Networks for Hybrid System Identification, Conference on Robot Learning (CoRL) 2019. (arxiv link)