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.
Highlights
P ATH planning is a cornerstone of robotics
In addition to the advantages in path planning that this method offers and above and beyond related works, we introduce an additional loss on the run-time activation maximisation (AM) optimisation process which encourages the planning process to remain in areas of high likelihood according to our prior belief under the generative model
Before extending to path planning with additional constraints, we explore the ability of iterative AM as described in Section III-C to produce a path plan for goal reaching in free space
Summary
This generally consists of producing a sequence of joint states the robot needs to follow in order to move from a start to a goal configuration. This requires that the poses along the sequence are kinematically feasible while at the same time avoiding unwanted contact either by the manipulator with itself or with potential objects in the robot’s workspace. While existing sampling and optimisation-based approaches to the planning problem can find solutions, they scale super-linearly with a robot’s degrees of freedom, and those that have optimality guarantees on resulting paths are guaranteed to achieve this only asymptotically, after infinite time [3]. Sampling-based planners, on the other hand, struggle to find solutions in scenarios where constraints render only a small volume of configuration space feasible or where narrow passages exist [7]
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