Publikation
FlowMP: Learning Motion Fields for Robot Planning with Conditional Flow Matching
Khang Nguyen; An T. Le; Tien Pham; Manfred Huber; Jan Peters; Minh Nhat Vu
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2503.06135, Pages 1-7, arXiv, 2025.
Zusammenfassung
Prior flow matching methods in robotics have
primarily learned velocity fields to morph one distribution of
trajectories into another. In this work, we extend flow matching
to capture second-order trajectory dynamics, incorporating
acceleration effects either explicitly in the model or implic-
itly through the learning objective. Unlike diffusion models,
which rely on a noisy forward process and iterative denois-
ing steps, flow matching trains a continuous transformation
(flow) that directly maps a simple prior distribution to the
target trajectory distribution without any denoising procedure.
By modeling trajectories with second-order dynamics, our
approach ensures that generated robot motions are smooth
and physically executable, avoiding the jerky or dynamically
infeasible trajectories that first-order models might produce.
We empirically demonstrate that this second-order conditional
flow matching yields superior performance on motion planning
benchmarks, achieving smoother trajectories and higher success
rates than baseline planners. These findings highlight the
advantage of learning acceleration-aware motion fields, as our
method outperforms existing motion planning methods in terms
of trajectory quality and planning success.