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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