AnyMug trains a single closed-loop visuomotor policy in simulation using observation-action canonicalization and deploys it zero-shot on a real robot for functional mug-handle grasping across poses.
Spatial robograsp: Generalized robotic grasping control policy
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GeoSem-WAM adds geometric and semantic auxiliary prediction tasks to World Action Models during training to improve latent representations and action prediction accuracy while keeping inference efficient by avoiding explicit future rollouts.
AttenA+ reweights action training objectives in VLA and WAM models via inverse velocity attention to prioritize kinematically critical segments, yielding small benchmark gains.
citing papers explorer
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Pose-Agnostic Robotic Functional Grasping via Observation-Action Canonicalization
AnyMug trains a single closed-loop visuomotor policy in simulation using observation-action canonicalization and deploys it zero-shot on a real robot for functional mug-handle grasping across poses.
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GeoSem-WAM: Geometry- and Semantic-Aware World Action Models
GeoSem-WAM adds geometric and semantic auxiliary prediction tasks to World Action Models during training to improve latent representations and action prediction accuracy while keeping inference efficient by avoiding explicit future rollouts.
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AttenA+: Rectifying Action Inequality in Robotic Foundation Models
AttenA+ reweights action training objectives in VLA and WAM models via inverse velocity attention to prioritize kinematically critical segments, yielding small benchmark gains.