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Affiliation:, Nanjing University of Science and Technology
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Robots can now reliably act on human gestures by anchoring interaction evidence to specific object instances, achieving over 2x success on implicit intent tasks compared to large VLM baselines.
Human-robot interaction anticipation models exhibit significant directional bias in cue transferability, with pose corrections boosting performance only when transferring from a less to a more visually diverse dataset.
Coordination mechanisms can significantly enhance the robustness of dual-action policies, but some methods may inadvertently increase false alarms.
