add-3d-end-effector-point-cloud-latent-actions TEC-0089 · proposedAdd 3D end-effector point-cloud latent actions to image-base
Learns a second discrete latent action code from start-goal end-effector point-cloud transitions, shared across embodiments via a unified bimanual code, pair-consistent random 3D transforms, and forward/backward transition training, and use
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Learns a second discrete latent action code from start-goal end-effector point-cloud transitions, shared across embodiments via a unified bimanual code, pair-consistent random 3D transforms, and forward/backward transition training, and use Filed by the drafting stage from GALA: Geometry-Aware Latent Action Modeling for Vision-Language-Action Model Pretraining across Embodiments (2609.21948) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Frozen-latent MLP probing of end-effector translation/rotation/finger angle error on a held-out XHand split; cross-embodiment grasp/hold/release retrieval (R@1 45.67 vs 33.37 without point clouds, 38.69 without the unification designs); RoboCasa GR-1 24-task success (55.7% vs 50.9% image-only, 54.4% without unification; 68.3% under multi-embodiment co-training); 4 real tasks, 50 trials each (75.5% vs 68.5% without unification, 71.5% HARP-VLA).. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
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