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T2-Z is introduced for test-time optimization of environment latents, which augments a robotic world model with a learnable environment latent to condition world predictions, and can efficiently infer environment latents from as little as a single test-time interaction trajectory.
Results demonstrate that generative video rendering mitigates the visual sim-to-real gap for zero-shot policy transfer, and shows that policy performance improves with more generated videos per simulation trajectory, increasing opening-task success by 65 percentage points.
Policies trained offline from teleoperated demonstrations can degrade outside their training distribution. This gap is particularly acute in long-horizon whole-body mobile manipulation, where long horizons amplify compounding errors and coordinated arm, torso, and base motion makes exhaustive demonstration coverage imp...
Memory-dependent robotic manipulation requires policies to use information that is no longer available in the current observation. Retaining history alone is insufficient: memory must preserve information that supports future actions. One challenge is whether a memory-free foundation model can learn to retain and use h...
RoboTTT, a robot model and training recipe that scale visuomotor context to 8K timesteps, three orders of magnitude beyond state-of-the-art policies, without growing inference latency, unlocks new robot capabilities: one-shot in-context imitation from human video demonstrations, on-the-fly policy improvement, robustnes...
TrAct is proposed, a world-model-based robot decision-making framework that uses visual tracks as an intermediate interface between control and prediction, enabling more accurate world modeling and stronger robot generalization.
Masked Visual Actions is introduced, a pixel-space control interface that expresses action as a partially revealed trajectory of an arbitrary entity in a video that supports inverse modeling by synthesizing robot motion from desired object motion.
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