Autonomously Acquiring Robot Manipulation Skills with Language-Driven Quality-Diversity
Research
Source: arXiv cs.ROPublish time unverified
arXiv:2608.30983v1 Announce Type: new Abstract: Quality-diversity (QD) algorithms have been gaining traction in robot learning, where diverse motion primitive libraries allow robots to adapt zero-shot to constraints at deployment time. However, such methods typically require expert designers to write the success condition, fitness and diversity metrics, and this strongly limits the robot's autonomy.