200个任务、1700万帧！大晓ACE-Data-0把真实家庭场景变成物理智能「数据引擎」
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Merged summary
TL;DR - Daxiao Robotics and NTU’s S-Lab open-sourced ACE-Data-0, a multimodal dataset capturing long-horizon human activity in real homes. It provides synchronized physical signals and benchmarks for training and evaluating embodied AI systems.
- Includes 150 hours, 17 million video frames, 200 task categories, 50 participants, and 75,000 interaction segments across two homes.
- Aligns egocentric and multi-view video, body and hand motion, object 6-DoF trajectories, audio, and tactile pressure in shared temporal and spatial coordinates.
- Uses goal-level instructions to preserve natural planning differences, hesitation, recovery, and task sequences lasting up to 20–30 minutes.
- Offers three benchmark levels covering contact inference, human/object state recovery, and hand-object interaction understanding; evaluations of 30-plus methods found gaps under occlusion, unusual viewpoints, self-motion, and long tasks.
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200个任务、1700万帧！大晓ACE-Data-0把真实家庭场景变成物理智能「数据引擎」
Public signals
N/A
TL;DR - Daxiao Robotics and NTU’s S-Lab open-sourced ACE-Data-0, a multimodal dataset capturing long-horizon human activity in real homes. It provides synchronized physical signals and benchmarks for training and evaluating embodied AI systems.
- Includes 150 hours, 17 million video frames, 200 task categories, 50 participants, and 75,000 interaction segments across two homes.
- Aligns egocentric and multi-view video, body and hand motion, object 6-DoF trajectories, audio, and tactile pressure in shared temporal and spatial coordinates.
- Uses goal-level instructions to preserve natural planning differences, hesitation, recovery, and task sequences lasting up to 20–30 minutes.
- Offers three benchmark levels covering contact inference, human/object state recovery, and hand-object interaction understanding; evaluations of 30-plus methods found gaps under occlusion, unusual viewpoints, self-motion, and long tasks.