Reaching Through Latent Space: From Joint Statistics to Path Planning in Manipulation
This addresses path planning for robotic manipulation, but it is incremental as it builds on existing generative models and optimization techniques.
The paper tackles robotic manipulator path planning by optimizing in the latent space of a generative model, incorporating constraints via classifiers, and achieves performance comparable to existing planners in task success, planning time, and path length on a real 7-DoF robot arm.
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.