arXiv Computer Vision
Language-Augmented Video Action Anticipation: Design Fundamentals, Benchmarks, and Open Challenges
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Action- and Language-Conditioned Video Assessment for Embodied Control
arXiv:2608.08273v2 Announce Type: replace-cross Abstract: Vision-based embodied agents executing multi-step natural language instructions require feedback mechanisms that assess task progress over co...
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PearlVLA: Progressive Embodied Action-Plan Refinement in Latent Space
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Foresight: Iterative Reasoning About Clues that Matter for Navigation
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Outcome-Conditioned End-Effector Geometry Across Vision-Language-Action Policies
The study examines how different vision‑language‑action (VLA) policies execute a manipulation task by comparing the geometry of their end‑effectors across 15,000 closed‑loop LIBERO rollouts. By pairing 3,600 configuration‑matched policy executions, the authors find that when both policies succeed, their end‑effector trajectories are much closer (median DTW distance 0.0120 m) than when only one succeeds (0.0380 m), a pattern consistent across all tasks, policy pairs, and nine representations. Even successful executions remain as far from same‑task demonstrations as the demonstrations are from each other, indicating that task‑associated geometry, rather than training data overlap, drives these differences.
arXiv Computer Vision
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Seeing is not Enough: Vision-Language Models Perceive Evidence but Fail to Act
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Act with Intent: Distilling Behavior Intent for Vision-Language-Action Models
The paper introduces Intention Distillation (INDI), a method that injects behavior-level intent into Vision‑Language‑Action (VLA) model decoders by leveraging a frozen teacher vision‑language model to interpret demonstrations. During training, the teacher processes the current observation, instruction, coarse action summary, and execution video, producing a multimodal intent representation that the VLA decoder uses alongside trajectory and execution features to predict actions. Experiments on SimplerEnv‑Bridge, RoboCasa Kitchen, and real‑world tasks show that INDI consistently improves success rates, especially on longer‑horizon tasks, demonstrating that explicit modeling of semantic intent benefits action decoders.