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We present a framework for visual action planning of complex manipulationtasks with high-dimensional state spaces, focusing on manipulation ofdeformable objects. We propose a Latent Space Roadmap (LSR) for task planningwhich is a graph-based structure globally capturing the system dynamics in alow-dimensional latent space. Our framework consists of three parts: (1) aMapping Module (MM) that maps observations given in the form of images into astructured latent space extracting the respective states as well as generatesobservations from the latent states, (2) the LSR which builds and connectsclusters containing similar states in order to find the latent plans betweenstart and goal states extracted by MM, and (3) the Action Proposal Module thatcomplements the latent plan found by the LSR with the corresponding actions. Wepresent a thorough investigation of our framework on simulated box stacking andrope/box manipulation tasks, and a folding task executed on a real robot.

Enabling Visual Action Planning for Object Manipulation through Latent Space Roadmap