Publikation
Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models
Jo~ao Carvalho; An T. Le; Mark Baierl; Dorothea Koert; Jan Peters
In: IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Pages 1916-1923, ArXiv, 2023.
Zusammenfassung
Learning priors on trajectory distributions can
help accelerate robot motion planning optimization. Given pre-
viously successful plans, learning trajectory generative models
as priors for a new planning problem is highly desirable.
Prior works propose several ways on utilizing this prior to
bootstrapping the motion planning problem. Either sampling
the prior for initializations or using the prior distribution in a
maximum-a-posterior formulation for trajectory optimization.
In this work, we propose learning diffusion models as priors.
We then can sample directly from the posterior trajectory
distribution conditioned on task goals, by leveraging the inverse
denoising process of diffusion models. Furthermore, diffusion
has been recently shown to effectively encode data multi-
modality in high-dimensional settings, which is particularly
well-suited for large trajectory dataset. To demonstrate our
method efficacy, we compare our proposed method - Motion
Planning Diffusion - against several baselines in simulated
planar robot and 7-dof robot arm manipulator environments.
To assess the generalization capabilities of our method, we
test it in environments with previously unseen obstacles. Our
experiments show that diffusion models are strong priors
to encode high-dimensional trajectory distributions of robot
motions. https://sites.google.com/view/mp-diffusion