Reproduce “A Flexible Field-Based Policy Learning Framework for Diverse Robotic Systems and Sensors” on OpenResearch

About this paper

Jose Gustavo Buenaventura Carreon, Floris Erich, Roman Mykhailyshyn, Tomohiro Motoda, Ryo Hanai, Yukiyasu Domae

Researchers at AIST developed a flexible field-based policy learning framework that extends visuomotor diffusion policies, specifically GenDP, to operate across varied robot-camera configurations. This framework demonstrated effective skill transfer for a grasp-and-lift task, achieving 80% success with a UR5 and Azure Kinect setup and 90% with an ALOHA and RealSense setup.

Key insights

• Decoupling the core learning and perception modules from hardware-specific interfaces is crucial for achieving cross-robot generalization in visuomotor policy learning.
• A modular configuration layer and modernized communication infrastructure enable robust policy training and evaluation across diverse robot-camera systems without extensive re-engineering.
• Visuomotor diffusion policies combined with 3D semantic scene representations can achieve high task success rates with relatively few demonstration episodes, even when adapted to new hardware.

Summary by alphaXiv.