Reaching Through Latent Space: From Joint Statistics to Path Planning in Manipulation

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. Opti...

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Bibliographic Details
Published in:	IEEE robotics and automation letters 2022-04, Vol.7 (2), p.5334-5341
Main Authors:	Hung, Chia-Man, Zhong, Shaohong, Goodwin, Walter, Jones, Oiwi Parker, Engelcke, Martin, Havoutis, Ioannis, Posner, Ingmar
Format:	Article
Language:	eng
Subjects:	Collision avoidance Constrained motion planning Constraint modelling deep learning in grasping and manipulation End effectors Iterative methods Obstacle avoidance Optimization optimization and optimal control Planning representation learning Robot arms Robots Task analysis Trajectory Trajectory planning
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Summary:	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.
ISSN:	2377-3766 2377-3766