VIP learns a visual embedding from human videos whose distance defines dense, smooth rewards for arbitrary goal-image robot tasks without task-specific fine-tuning.
E.3.1 R OBOT AND OBJECT POSE ERROR ANALYSIS In this section, we visualize the per-step robot and object poseL2 error with respect to the goal-image poses
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VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training
VIP learns a visual embedding from human videos whose distance defines dense, smooth rewards for arbitrary goal-image robot tasks without task-specific fine-tuning.