arXiv AI
WAM-OPD: On-Policy Distillation for World Action Models
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arXiv AI
Oct 5
WAM-OPD: Joint Video-Action Supervision for World Action Model Post-Training with On-Policy Distillation
WAM-OPD introduces a method for post‑training improvement of World Action Models (WAMs) by collecting student rollout histories and querying a stronger teacher for paired video and action targets. The student learns from both modalities while preserving its one‑step generation capability at deployment. Experiments on 12 RoboTwin 2.0 tasks and four real‑robot tasks show success rates rising from 33.8% to 65.7% and from 51.4% to 64.6%, respectively, with joint video‑action supervision outperforming single‑modality approaches.
arXiv Computer Vision
Sep 30
EVO-WAM: Evolving World Action Models through Video-Action Verification
arXiv:2609.38057v1 Announce Type: new Abstract: Improving robot policies on new tasks without collecting additional expert demonstrations remains a central challenge in robot learning. World action m...
arXiv Computer Vision
6d ago
Video Prediction Policy 2: Predict Better, Act Better
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 Computer Vision
Sep 21
MT-WAM: Reorienting the One-Pass Predictive Representation Toward Action Generation
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 AI
Aug 13
Foresight Without Seeing: Latent Futures for World Action Models
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 Computer Vision
Oct 5
Native Action-Prior Learning from Videos for World Action Models
The paper introduces NAVA-WAM, a method that learns action priors directly from observation-only videos to pretrain action policies for robots. It uses a two-stage training process: first, it pretrains on videos with future‑video flow‑matching supervision to learn action‑relevant priors, and second, it fine‑tunes with action‑labeled demonstrations for robot control. Experiments show that NAVA‑WAM outperforms previous methods in both in‑distribution and out‑of‑distribution scenarios, achieving strong action‑label efficiency and real‑robot generalization.