Publikation
Motion Planning Diffusion: Learning and Adapting Robot Motion Planning With Diffusion Models
Jo~ao Carvalho; An T. Le; Piotr Kicki; Dorothea Koert; Jan Peters
In: IEEE Transactions on Robotics (T-RO), Vol. 41, Pages 4881-4901, arXiv, 2025.
Zusammenfassung
The performance of optimization-based robot mo-
tion planning algorithms is highly dependent on the initial
solutions, commonly obtained by running a sampling-based
planner to obtain a collision-free path. However, these methods
can be slow in high-dimensional and complex scenes and produce
non-smooth solutions. Given previously solved path-planning
problems, it is highly desirable to learn their distribution and
use it as a prior for new similar problems. Several works
propose utilizing this prior to bootstrap the motion planning
problem, either by sampling initial solutions from it, or using its
distribution in a maximum-a-posterior formulation for trajectory
optimization. In this work, we introduce Motion Planning Dif-
fusion (MPD), an algorithm that learns trajectory distribution
priors with diffusion models. These generative models have
shown increasing success in encoding multimodal data and have
desirable properties for gradient-based motion planning, such as
cost guidance. Given a motion planning problem, we construct
a cost function and sample from the posterior distribution using
the learned prior combined with the cost function gradients
during the denoising process. Instead of learning the prior on all
trajectory waypoints, we propose learning a lower-dimensional
representation of a trajectory using linear motion primitives,
particularly B-spline curves. This parametrization guarantees
that the generated trajectory is smooth, can be interpolated at
higher frequencies, and needs fewer parameters than a dense
waypoint representation. We demonstrate the results of our
method ranging from simple 2D to more complex tasks using
a 7-dof robot arm manipulator. In addition to learning from
simulated data, we also use human demonstrations on a real-
world pick-and-place task. The experiment results show that
diffusion models are strong priors for encoding multimodal
trajectory distributions for optimization-based motion planning.
https://sites.google.com/view/motionplanningdiffusion