MT‑WAM enhances the Fast‑WAM framework by adding complementary supervision for future 2‑D point trajectories and visual features while keeping the original training objectives. A lightweight dual‑stream branch and structured attention mask isolate motion‑specific processing, and motion‑stream tokens provide additional dynamics cues to the action expert. During inference, MT‑WAM skips future‑video prediction, using cached video and motion information to achieve higher success rates on LIBERO, LIBERO‑Plus, RoboTwin 2.0 Clean2Rand, and several real‑world tasks.
arXiv:2608. 11605v1 Announce Type: new Abstract: World Action Models (WAMs) couple future visual prediction with robot action generation, enabling policies to model how the physical world evolves during interaction.
arXiv:2609.23369v1 Announce Type: new Abstract: Generation-free world action models (WAMs) retain future-video prediction during training but act from internal video features at inference, leaving un...
arXiv:2610.28122v1 Announce Type: cross Abstract: Cascade world-action models (WAMs) use predicted visual futures to condition robot actions, but inherit costly iterative inference from diffusion-pre...
Video Prediction Policy 2 (VPP2) is a new world action model that improves zero‑shot generalization for both video prediction and action generation. It achieves this by pretraining a large, diverse manipulation video dataset with event‑level supervision, then distilling the model into a single‑step visual planner and adding a mixture‑of‑transformers action module. Experiments show VPP2 outperforms leading baselines on open‑ended video prediction, real‑world zero‑shot manipulation, and several challenging benchmarks.
arXiv:2610.16443v1 Announce Type: new Abstract: Recent world action models draw on video generation priors to jointly predict future observations and robot actions. Adapting these priors to control r...
arXiv:2609.37250v1 Announce Type: cross Abstract: World-action models (WAMs) couple future visual-state prediction with action generation. By adapting video generators or image-editing models pretrai...
CtrlWAM introduces a controllable world action model that jointly predicts actions (intent) and visual futures (foresight). By executing perturbed actions in a simulator and pairing them with noised visual outcomes, it aligns action predictions with their visual consequences, using warped video–action noise schedules to maintain visual layout responsiveness. The model extends beyond ego‑only control to multiple agent streams, improving action forecasts, video–action agreement, and command following in driving and robotics experiments.
arXiv:2610.02508v1 Announce Type: new Abstract: World action models (WAMs) have emerged as a promising paradigm for robotic control by jointly predicting future visual dynamics and actions from an in...
The paper introduces Motion-Guided Asymmetric Prediction (MoGAP), a method that enhances World Action Models (WAMs) for robot control by focusing visual supervision on dynamic regions of video data. MoGAP asymmetrically corrupts static and dynamic areas, training the model to reconstruct heavily corrupted dynamic regions using less corrupted static context, all within the shared video diffusion backbone. Experiments on robot manipulation benchmarks show that MoGAP consistently improves policy performance, raising success rates on the GR-1 tabletop benchmark from 38.8% to 47.5% for Fast‑WAM and from 52.3% to 62.3% for Cosmos3‑Nano.
SimWAM is a lightweight World-Action Model that uses future‑video prediction only during training to supervise an action expert, enabling end‑to‑end autonomous driving without costly test‑time future imagination. The architecture co‑trains a pretrained video expert and a lightweight action expert via joint flow matching, while an isolated attention mask keeps action prediction independent of future frames. This design allows the video backbone to be swapped and the action expert scaled independently, achieving 91.5 PDMS on NAVSIM, outperforming state‑of‑the‑art WAM planners with lower latency and zero‑shot transfer to nuScenes.
arXiv:2609.39324v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have recently incorporated world models to provide richer dynamic supervision beyond sparse action labels. Howeve...
WAM-Cache is a training‑free framework that reuses layerwise key‑value (KV) representations from a pretrained video Diffusion Transformer (DiT) across control chunks, recomputing only a sparse set of refreshed tokens. By selecting refresh tokens based on the action expert’s cross‑attention, visual latent surprise, and a strict age bound, it reduces DiT prefill FLOPs by 32‑42% on datasets such as RoboTwin 2.0 and LIBERO while maintaining action accuracy within 0.7‑1.8 percentage points of the dense baseline in simulation and 2.5 points on a real robot.