3 accepted papers
This paper proposes a simple yet effective markerless hand-eye calibration method that achieves low cost, high accuracy, and strong generalization across different types of robots. The method utilizes a circular flange, a standardized structure in industrial robots, for calibration via the perspecti…
We propose Observer-Actor (ObAct), a novel framework for active vision imitation learning in which the observer moves to optimal visual observations for the actor. We study ObAct on a dual-arm robotic system equipped with wrist-mounted cameras. At test time, ObAct dynamically assigns observer and ac…
We present One-Shot Affordance Learning (OSAL): a unified pipeline that learns manipulation for articulated objects by observing human demonstration only once. The key idea of our method is to embody affordance of articulated objects with an open-loop trajectory conditioned on a certain area of the…