arXiv:2609.10506v1 Announce Type: cross Abstract: Action-conditioned latent world models predict future visual representations, enabling zero-shot goal-conditioned robot planning and control. However...
arXiv:2610.02840v1 Announce Type: cross Abstract: World action models jointly learn to forecast world dynamics and predict robot actions, such that the learned internal world dynamics guide accurate...
Being‑M0.7 is a latent world‑action model that learns from a large mixed‑modality human dataset (over 10,000 hours of video‑only, motion‑only, and paired video‑motion data) to predict future latent visual states and motion. The model is pre‑trained on human data, adapted to robot viewpoints during mid‑training, and then combined with an action expert via gated cross‑attention during post‑training to produce executable whole‑body commands. In experiments, Being‑M0.7 outperforms baselines on the SIMPLE benchmark and matches the best baseline on real‑world Unitree G1 loco‑manipulation tasks.
ForeAct3D is a framework that grounds future world modeling in Vision‑Language‑Action policies by decoding depth, semantic segmentation, and camera pose from policy representations into current and future 3D scene states. The future queries are conditioned on the policy‑generated action chunk, and a physical‑consistency closure enforces background staticity, instance‑level rigidity, and wrist‑camera pose alignment with end‑effector kinematics. This approach eliminates the need for future prediction at inference and achieves high success rates on LIBERO and CALVIN, outperforming the base policy across all suites.
arXiv:2610.29246v1 Announce Type: cross Abstract: Predictive representations for robot manipulation should connect scene geometry with the state changes induced by actions. Our intuition is that unde...
FOCAL‑VLA is a framework that improves vision‑language‑action models by combining subtask‑guided geometry distillation with implicit world modeling. It transfers geometric knowledge from VGGT to focus on subtask‑relevant image regions and uses Track4World features to capture future 3D evolution, guiding action generation without running these models at inference time. Experiments demonstrate that FOCAL‑VLA outperforms baselines on both simulation benchmarks and real‑world manipulation tasks.
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:2608.22067v1 Announce Type: cross Abstract: World-Action Models (WAMs) build robot control on video-generation backbones, which jointly predict dense future visual trajectories and robot action...
MAVP (Map-Aware Visuomotor Policies) is a framework that enhances mobile manipulation by predicting explicit base-pose targets and tracking them with localisation feedback. It reconstructs a static map from teleoperated demonstrations, expressing base trajectories in a shared map frame to provide consistent spatial supervision. During execution, the policy uses RGB observations, joint states, and the robot’s current map-frame base pose to jointly predict target base poses, arm actions, and gripper actions, while a low‑level controller corrects deviations using feedforward motion and pose error feedback. Pose‑noise augmentation during training further improves robustness, and MAVP outperforms unanchored velocity control across six real‑world tasks and three policy families.
arXiv:2609.16864v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have achieved impressive performance in quasi-static manipulation, but struggle in dynamic manipulation tasks bec...
World models offer a data-driven alternative to traditional simulators for robotics, with applications spanning policy evaluation, improvement, and planning. All of these uses depend on faithful 3D ge...
arXiv:2610.06598v2 Announce Type: replace-cross Abstract: Action-conditioned robot world models must respond precisely to robot trajectories while preserving realistic visual dynamics, yet learning b...