Reaching Through Latent Space: From Joint Statistics to Path Planning in Manipulation
Chia-Man Hung, Shaohong Zhong, Walter Goodwin, Oiwi Parker Jones, Martin Engelcke, Ioannis Havoutis, Ingmar Posner
Abstract
We present a novelapproach to path planning for robotic manipulators, in which paths are produced via iterative optimisation in the latent space of a generative model of robot poses. Constraints are incorporated through the use of constraint satisfaction classifiers operating on the same space. Optimisation leverages gradients through our learned models that provide a simple way to combine goal reaching objectives with constraint satisfaction, even in the presence of otherwise non-differentiable constraints. Our models are trained in a task-agnostic manner on randomly sampled robot poses. In baseline comparisons against a number of widely used planners, we achieve commensurate performance in terms of task success, planning time and path length, performing successful path planning with obstacle avoidance on a real 7-DoF robot arm.
BibTeX
@inproceedings{ral2022_reachingthroughl,
title = {Reaching Through Latent Space: From Joint Statistics to Path Planning in Manipulation},
author = {Chia-Man Hung and Shaohong Zhong and Walter Goodwin and Oiwi Parker Jones and Martin Engelcke and Ioannis Havoutis and Ingmar Posner},
booktitle = {RA-L 2022},
year = {2022}
}