We present a framework for visual action planning of complex manipulation tasks with high-dimensional statespaces, focusing on manipulation of deformable objects. Wepropose a Latent Space Roadmap (LSR) for task planningwhich is a graph-based structure globally capturing the systemdynamics in a low-dimensional latent space. Our frameworkconsists of three parts: (1) a Mapping Module (MM) that mapsobservations given in the form of images into a structuredlatent space extracting the respective states as well as generatesobservations from the latent states, (2) the LSR which builds andconnects clusters containing similar states in order to find thelatent plans between start and goal states extracted by MM, and(3) the Action Proposal Module that complements the latent planfound by the LSR with the corresponding actions. We presenta thorough investigation of our framework on simulated boxstacking and rope/box manipulation tasks, and a folding taskexecuted on a real robot. ; QC 20211109
Enabling Visual Action Planning for Object Manipulation through Latent Space Roadmap
Paper
Electronic Resource
English
DDC: | 629 |
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