Continuous-time Gaussian process motion planning via probabilistic inference

By Mustafa Mukadam, Jing Dong, Xinyan Yan, Frank Dellaert, Byron Boots. 2017

283 citations recorded by the source corpus.

We introduce a novel formulation of motion planning, for continuous-time trajectories, as probabilistic inference. We first show how smooth continuous-time trajectories can be represented by a small number of states using sparse Gaussian process (GP) models. We next develop an efficient gradient-based optimization algorithm that exploits this sparsity and GP interpolation. We call this algorithm the Gaussian Process Motion Planner (GPMP). We then detail how motion planning problems can be formulated as probabilistic inference on a factor graph. This forms the basis for GPMP2, a very efficient algorithm that combines GP representations of trajectories with fast, structure-exploiting inference via numerical optimization. Finally, we extend GPMP2 to an incremental algorithm, iGPMP2, that can efficiently replan when conditions change. We benchmark our algorithms against several sampling-based and trajectory optimization-based motion planning algorithms on planning problems in multiple environments. Our evaluation reveals that GPMP2 is several times faster than previous algorithms while retaining robustness. We also benchmark iGPMP2 on replanning problems, and show that it can find successful solutions in a fraction of the time required by GPMP2 to replan from scratch.

Subfields: Aerospace Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition, Probabilistic & Bayesian Methods, Robotics & Control

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Citations in the corpus

Habitat 2.0: Training Home Assistants to Rearrange their Habitat (2021, background): “[89] Mustafa Mukadam, Jing Dong, Xinyan Yan, Frank Dellaert, and Byron Boots.”
Towards Robust Skill Generalization: Unifying Learning from Demonstration and Motion Planning (2017, methodology): “In an earlier application of this view on motion planning [4, 5], the trajectory prior was simple and pre-defined: it encouraged trajectories that minimize acceleration.”
Differentiable Gaussian Process Motion Planning (2019, methodology): “However, we do not compare against other sampling and optimization-based planners and refer the reader to [3] for benchmarks of GPMP2 against leading sampling and optimization-based planners.”