Year: 2026
Type: conference-paper
Abstract: Diffusion models have recently emerged as a powerful approach for robot motion planning, capable of generating multimodal, high-quality trajectories.Their main limitation is slow sampling, which hinders real-time replanning in dynamic environments.This work studies several acceleration strategies for diffusion-based motion planning and leverages the resulting speedups to enable replanning at up to 100 Hz in a dynamic benchmark with randomly moving obstacles.A cost-based trajectory selection mechanism is used to exploit multimodality during replanning by balancing motion time, predicted collision risk, and smoothness.Comparisons against warm-start diffusion and MPC show that accelerated diffusion models achieve the best performance in dynamic settings.
Source: Lirias
Institution: KU Leuven
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7165075053