Safe robotic task planning requires planners that can reliably achieve task goals while satisfying safety constraints. Classical planners search over explicit symbolic task representations and can provide formal guarantees with respect to encoded safety constraints, but require rigid input formats. Their computational...
Wei-Zhe Xu, Jia-Liang Fan, Essien Taylor et al.· 0 citations
This work introduces RedFlow, an offline post-training method for flow-matching VLA policies that requires neither external HIL corrections nor online data collection during post-training, and improves average success across four LIBERO suites.
PAVXploreRL is a reinforcement learning framework built on a pretrained latent world model that explicitly optimizes PAV objectives through reward-driven training, and jointly leverages ID trajectories and noise-driven OOD action exploration, without paired video supervision.