# Robota-Embed-Bench

When a robot draws on past experience, matching usually relies on task or scene similarity, which cannot separate execution stages such as approaching a bowl from having already placed something in it. Robota-Embed encodes language, vision and native action together in both the current-state query and the recorded future clip, so motion enters the retrieval key itself. On Robota-Embed-Bench, 4,000 queries against a shared 8,000-clip gallery, it reaches 47.33% clip-level and 81.77% episode-level R@1; the abstract reports gains of 22.09 and 33.47 percentage points over the action-free Gemini Embedding 2, while Section 4.1 gives 22.09 and 33.18 points.

When a robot draws on past experience, matching usually relies on task or scene similarity, which cannot separate execution stages such as approaching a bowl from having already placed something in it. Robota-Embed encodes language, vision and native action together in both the current-state query and the recorded future clip, so motion enters the retrieval key itself. On Robota-Embed-Bench, 4,000 queries against a shared 8,000-clip gallery, it reaches 47.33% clip-level and 81.77% episode-level R@1; the abstract reports gains of 22.09 and 33.47 percentage points over the action-free Gemini Embedding 2, while Section 4.1 gives 22.09 and 33.18 points.