Extended Data Fig. 1: Self correction on real world experiment.
From: What matters in building vision–language–action models for generalist robots
Visualization for rollouts that the best setting VLA built by RoboVLMs emerges the ability of self-correction. For instance, in the Open The Oven task, the robotś first attempt does not reach the oven handle, and it adjusts the end-effector position to re-locate the handle at the second attempt. Note that the training dataset does not contain this kind of data.
