Reaching Through Latent Space: From Joint Statistics to Path Planning in
Manipulation
- URL: http://arxiv.org/abs/2210.11779v1
- Date: Fri, 21 Oct 2022 07:25:21 GMT
- Title: Reaching Through Latent Space: From Joint Statistics to Path Planning in
Manipulation
- Authors: Chia-Man Hung, Shaohong Zhong, Walter Goodwin, Oiwi Parker Jones,
Martin Engelcke, Ioannis Havoutis, Ingmar Posner
- Abstract summary: We present a novel approach to path planning for robotic manipulators.
Paths are produced via iterative optimisation in the latent space of a generative model of robot poses.
Our models are trained in a task-agnostic manner on randomly sampled robot poses.
- Score: 26.38185646091712
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: We present a novel approach 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.
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