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Scaling generalist robotic policies is limited by the high cost of collecting embodied interaction data. Large-scale egocentric human videos offer a rich alternative, but they lack robot action labels, and existing action annotation methods are often noisy under real-world visual complexity and diverse manipulation dyn...
This work pretrain MM-ABC, a foundation model built around Seeing, Coordinating, and Imagining Arm-Base Collaboration, which combines sparse multi-level VLM features for spatial perception; a training-only future branch that uses world imagination and geometric intent as extra supervision, strengthening perception and...
MoPA is presented, a framework that aligns perceptual conditioning with mobility and manipulation while preserving coordination at the action level, and achieves state-of-the-art performance across all three task suites.
GeoLAM, a framework for learning geometry-grounded latent actions from action-free human videos, combines future-frame reconstruction through a frozen geometric feature hierarchy with motion supervision from a training-only 4D geometry teacher and requires neither the geometry teacher nor future-video generation.
Active exploration and semantic navigation require an embodied agent to build memory from partial observations, predict how the evolution of observed spatial memory may support future motion, and convert that prediction into actionable plans. We present GLAM, a goal-conditioned latent world model trained over global sp...
CheckVLA is proposed, which verifies execution with a separately trained, frozen action-conditioned world model, and results support action-conditioned verification as a way to restore feedback during chunked execution while keeping the repair consistent with inference latency.
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