arXiv:2608. 20114v1 Announce Type: new Abstract: Mobile manipulation requires a robot to predict how locomotion and arm motion jointly alter future observations and control.
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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.
ZimaBlue is a scalable framework that learns generalizable World Action Models (WAMs) from large-scale egocentric videos. It follows a three-stage curriculum: causal video pre‑training, video‑action mid‑training with a unified action representation, and final specialization to a target robot. The system employs an asynchronous Slow‑Fast architecture to enable real‑time 30 Hz action prediction, achieving a jump in real‑robot zero‑shot success from 36.1% to 77.8% when leveraging over 120,000 hours of embodied video.
HWAM (Humanoid World Action Model) jointly generates reference actions and the robot’s post‑execution proprioceptive state, bridging the action–execution gap in hierarchical humanoid systems. It integrates three conditional paths—policy denoising, forward dynamics modeling, and inverse dynamics modeling—to connect policy references, realized motion, and visual outcomes. On the LimX OLI humanoid, HWAM outperforms baselines, achieving a 70.6% success rate on Candy Picking versus 43.3% for Fast‑WAM.
The paper introduces HWAM, a Humanoid World Action Model that jointly generates reference actions and the robot’s post‑execution proprioceptive state, thereby closing the action‑execution gap in hierarchical humanoid systems. HWAM is trained via three conditional paths—policy denoising, forward dynamics modeling, and inverse dynamics modeling—to align policy outputs, executed motion, and visual outcomes. Experiments on the LimX OLI humanoid show HWAM achieving a 70.6% success rate on Candy Picking, outperforming Fast‑WAM’s 43.3%.
arXiv:2606. 09811v1 Announce Type: cross Abstract: World-action models have emerged as a promising paradigm for robot manipulation, jointly modeling visual scene dynamics and actions to inject physical priors into policy learning.
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.