DeltaWAM: Delta World Action Models for Bimanual Manipulation

DeltaWAM, a new world-action model from the AIGeeksGroup, improves average success on the RoboTwin benchmark over Fast-WAM from 81.3% to 85.4% in the clean setting and from 75.8% to 83.9% under visual randomization, according to the arXiv paper arXiv:2609.28811v1. DeltaWAM jointly predicts visual deltas and actions using dense-anchor, sparse-delta, and action streams, and its Streaming Delta Memory (SDM) reduces one-step inference latency and FLOPs by 36.57% and 31.55% respectively, while the three proposed architectures cut training FLOPs by 17.78-23.77%. Code is available at https://github.com/AIGeeksGroup/DeltaWAM.

arXiv:2609.28811v1 Announce Type: new Abstract: World-action models WAMs transfer visual and motion priors from pretrained video generators to robot control by jointly modeling visual dynamics and actions. Existing WAMs, however, predict dense future frames during training, repeatedly modeling largely unchanged content and coupling action-conditioned dynamics to nuisance appearance variations. At inference, processing each complete observation with the heavy video expert bottlenecks few-step action generation. Accordingly, we propose DeltaWAM, which jointly predicts visual deltas and actions using dense-anchor, sparse-delta, and action streams, with three architectures that differ in representation and computation sharing. We further develop Streaming Delta Memory SDM , which updates cached anchor context with compact observed deltas, reducing heavy video-expert processing. On RoboTwin, DeltaWAM with SDM improves average success over Fast-WAM from 81.3% to 85.4% in the clean setting and from 75.8% to 83.9% under visual randomization. The three architectures reduce training FLOPs by 17.78-23.77%, while SDM reduces one-step inference latency and FLOPs by 36.57% and 31.55%, respectively; real-world evaluations further show the highest overall success rate and normalized progress among the evaluated policies. Code: https://github.com/AIGeeksGroup/DeltaWAM. Website: https://aigeeksgroup.github.io/DeltaWAM.