OpenWAM: An Open Framework for Composable World-Action Models
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A framework for world–action models Should a robot predict what it will see before deciding how to act, or generate both together? World–action models make both possible. Comparing these choices is difficult when every system uses a different backbone, dataset, and training recipe. OpenWAM gives them a common foundation so we can study how prediction and control work together. The framework supports composition within a model and between models. We can change the order in which video and actions are generated and how their tokens attend to one another. We can also connect independently trained components: a video predictor proposes a future, an inverse dynamics model turns it into actions, and a forward dynamics model predicts what a supplied action sequence will do. Figure 1. OpenWAM overview.
A shared video foundation supports configurable video–action programs and independently trained dynamics. In the transfer experiments at bottom right, we adapt the video predictor to each task and reuse the IDM without further training.View full-size figure ↗ A shared video–action architecture Before learning actions, we adapt Wan2.2-5B to robot motion and interaction. We pretrain on approximately 3.34 million trajectories and recordings—14.64k hours of source video—spanning real and synthetic robot manipulation, human-guided manipulation, and human interaction.
This stage uses video alone, without action labels or proprioceptive inputs. What goes into pretraining? DatasetTrajectories / takesHours Open X-Embodiment1,300,7491,911.29 AgiBot World Beta1,003,6722,976.4 Ego-Exo4D v25,035221.26 InternData-A1637,4987,433.91 RoboCOIN183,1571,306.83 RoboMIND107,877305.5 FastUMI-100K92,823461.77 UMI family5,43024.60 Paper Table 8 · Hours count source sequences before training-window sampling, including synthetic and human-interaction video. OXE covers 49 manipulation datasets; InternData-A1 is synthetic; Ego-Exo4D records human interaction. The UMI family includes UMI, DexUMI, UMI on Legs, and MV-UMI.
Causal attention lets us generate video a chunk at a time: each chunk can use current and past observations and earlier chunks, but not later ones. Its tokens are denoised together. After 14 days on 32 NVIDIA B200 GPUs, this checkpoint provides the visual foundation for the downstream models. To add robot control, we pair the 5B video expert with a 2B action expert in a Mixture-of-Transformers (MoT) architecture. The action expert starts from width-adapted copies of the pretrained video layers. Each expert keeps its own normalization, projections, and feed-forward layers; attention over their combined tokens lets them exchange information.
Figure 2. Shared MoT architecture. A 5B video expert and a 2B action expert share attention over video and action tokens. Each also attends to the task instruction.View full-size figure ↗ Video-action interaction programs The same architecture can predict video before actions, actions before video, or both at once. Each interaction program specifies the generation order and attention between future tokens. The backbone, tokenization, training objective, and downstream recipe stay fixed, and every program receives the task instruction and observed history. ProgramGeneration and conditioning Video-then-action (VTA)Predict video first, then generate actions conditioned on that video. Action-then-video (ATV)Generate actions first, then predict video conditioned on those actions. JointDenoise video and actions together, with attention in both directions. DecoupledPredict video and actions without attention between their future tokens. Figure 3. Attention masks. A–D: the evaluated policy programs. E–F: local inverse and forward dynamics. Colors mark clean or noisy conditioning; numbers mark generation order. L denotes language; O−, O0, and O+ denote past, current, and future observations; A+ denotes future actions.View full-size figure ↗ All programs use latent flow matching, with four latent video frames aligned to each 16-step action chunk and proprioception supplied per chunk. In VTA and ATV, the second stage learns from recorded trajectories during training and uses the first stage’s predictions at inference. Beyond policies: local-context IDM and FDM A task-conditioned predictor proposes what should happen next.
Dynamics models connect that proposal to the robot’s motion: inverse dynamics (IDM) turns a visual future into actions, while forward dynamics (FDM) predicts the outcome of an action sequence. OpenWAM supports both as standalone models. We give them a local-context interface: the current observation, proprioception, and a supplied future trajectory. Local-context IDM: current observation + proprioception + supplied future video → action trajectory. Local-context FDM: current observation + proprioception + supplied action trajectory → future video. Neither model receives task language or pre-start history. This separates choosing a task from modeling a transition: we can adapt the video predictor and ask whether the same IDM still produces the right actions. The experiments below test how far this local information can take us. The video predictor passes VAE video latents to the IDM, not transformer hidden states or caches. With compatible video and action representations, the components can be trained separately and connected at inference. An FDM can likewise predict the outcome of actions from a separate policy. Learning from alternative outcomes A demonstration shows what the demonstrator chose to do, but says little about what other actions would have caused. Changing a model’s inputs does not fill that gap in its training data. We build LIBERO-Long-CF: 32,000 counterfactual segments across ten tasks by restoring simulator states and trying alternative action sequences, including unsuccessful ones. The models learn from the resulting observations and actions, without access to simulator state.
QuantityDemonstrationsLIBERO-Long-CF Tasks1010 Stored sequences50032,000 Sequences per task503,200 Controls per sequence276.2 mean128 Total controls138,0904,096,000 Control-equivalent hours1.9256.9 Paper Table 9 · Equivalent durations at 20 Hz.
Each counterfactual segment contains 128 controls and 129 synchronized two-view observations. The dataset contains 29.7× as many control records as the demonstrations, using the original tasks, assets, and physics. What changes in the counterfactual rollouts? We vary motion magnitude, direction, timing, individual action axes, and gripper behavior. 75% of segments start along a demonstration; the other 25% start after an additional action perturbation.
Intervention familyFraction (%) Stop / rescale arm motion9.4 Reverse / redirect translation6.3 Axis biases and pulses12.5 Dedicated yaw perturbation3.1 Noise / randomized arm controls12.5 Dedicated gripper interventions31.3 Random-duration arm / gripper interventions25.0 Intervention recipes (%) · Paper Table 10.
Values are rounded; different recipes can produce overlapping physical effects. In the perturbed starts we analyzed, the end effector is on average 3.34 cm from the nearest point on the demonstrated path (median 1.90 cm). Objects also move beyond their demonstrated configurations in 61.6% of these starts, measured at thresholds of 1 cm translation, 5° rotation, or 5% articulated-joint travel.
Measured interactionRate (%) Gripper–object / fixture contact90.4 Detected grasp50.0 Object-configuration effect72.3 Interaction rates (%) · Paper Table 11.
Measured over the segments analyzed; a segment can count toward multiple categories. Object changes are measured against the reference rollout from the same starting state. Branches from the same starting state stay together in the train/test split. Each transition supplies its own training example; the loss does not directly contrast pairs of branches. We compare models trained on demonstrations alone, counterfactuals alone (CF-only), and a mixture of 60% counterfactuals and 40% demonstrations.
Policy performance across programs LIBERO VTA achieves 98.6% mean success across four LIBERO suites. Each evaluated program exceeds 95% on LIBERO-Long. MethodObjectGoalSpatialLongMean OpenVLA88.479.284.753.776.5 OpenVLA-OFT98.497.997.694.597.1 π098.895.896.885.294.1 π0.598.298.098.892.496.9 GR00T-N197.693.094.490.693.9 Motus99.896.696.897.697.7 Fast-WAM100.097.098.295.297.6 LingBot-VA99.697.298.598.598.5 OpenWAM-VTA99.4 ± 0.398.4 ± 0.598.6 ± 0.297.8 ± 0.498.6 OpenWAM-ATV98.0 ± 0.497.2 ± 0.296.6 ± 0.695.4 ± 0.396.8 OpenWAM-Joint98.2 ± 0.297.8 ± 0.497.6 ± 0.396.6 ± 0.597.6 OpenWAM-Decoupled99.0 ± 0.398.0 ± 0.297.8 ± 0.597.0 ± 0.498.0 Closed-loop success (%) · Paper Table 3. OpenWAM: mean ± standard deviation over three training seeds, with 50 episodes per task and 500 per suite per seed. Baselines are reported as published in their respective papers. Decoupled remains competitive with Joint on LIBERO-Long: 97.0% versus 96.6%. Strong control on this benchmark does not require attention between future video and action tokens.
Bimanual manipulation On a bimanual robot, VTA and Joint each average about 92% success across toasting bread, completing the final layer of a 2 × 2 Rubik’s cube, and sorting cups by color. The setup uses two Franka Research 3 arms with parallel-jaw grippers, two wrist cameras, and a third-person camera.
Figure 4. Evaluation tasks. Left: one successful rollout per real-world task. Right: the four LIBERO-90 transfer tasks, outside the LIBERO-Long source set.View full-size figure ↗ MethodToastCubeCupsMean OpenWAM-VTA92.090.094.492.1 OpenWAM-Joint90.094.091.791.9 Closed-loop success (%) · Paper Table 4.
We train on 200 toast, 200 cube, and 180 cup demonstrations, each about 30 seconds, and evaluate on 50, 50, and 36 trials per method, respectively. What do pretraining and MoT contribute? Starting from a general-purpose video model helps, but adapting it to robot video makes a substantial difference.