Multi-skill ManipulationExpert
DROM: A Language-Guided Diffusion Framework for Multi-Skill Robotic Manipulation
This preprint introduces DROM, a language-guided diffusion policy that learns 17 manipulation skills from only 1–4 human demonstrations per skill, using Dynamic Movement Primitives to expand each into 300 synthetic trajectories. A single temporal U-Net with cross-attention generates skill-consistent motions on Franka Panda and FANUC CRX25ia arms, and long-horizon tasks are handled by an LLM that sequences skills; end-to-end task success ranges from 60.9% to 86.0% depending on task. It matters for industrial robotics because it cuts manual data collection and enables natural-language task programming across multiple hardware platforms.