DataPack
EgoCentric — Household Tasks
Train embodied AI and spatial systems to understand first-person household tasks — navigation context, object interaction, room transitions, and real-world environmental cues.
Solution
Robots fail in the real world when training data does not capture enough variation in environments, objects, lighting conditions, and human interaction to generalize beyond the lab.
Flikforge engineers embodied AI training data with the spatial, temporal, and egocentric annotations physical systems actually need — structured, rights-cleared, and GPU-ready.
Browse Relevant DataPacks →The Core Problem
Robotics teams collect hours of egocentric video. Models still fail because the footage lacks what makes action learnable — room context, mount position, object state, and task sequence. That gap is task blindness.
Performance Gaps Addressed
Room misclassification, mount ambiguity, acoustic blindness. Fixed by 78-parameter ground-truth labeling across 20K egocentric segments.
8 task-state fields per shot — task type, action sequence, step index, completion tracking — so models learn sequences, not isolated frames.
54-field scene-level annotation for multi-shot coherence — the structure long-horizon tasks require.
Room size, reverb, ambient sound, IMU — 28 audio parameters for navigation and manipulation models.
Raw egocentric video is plentiful. The structured task signal that makes it learnable is not. These DataPacks add the environment metadata, camera perspective, object-state changes, human interaction cues and task-sequence structure physical AI systems need to generalize beyond the lab.
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DataPack
Train embodied AI and spatial systems to understand first-person household tasks — navigation context, object interaction, room transitions, and real-world environmental cues.
DataPack
Train physical AI systems to understand first-person industrial workflows — material handling, boxing, palletizing, load transfer, workstations, and structured logistics environments.
DataPack
Train robots to understand fast-moving commercial service environments — human interaction, guest areas, spatial navigation, service workflows, and dynamic shared spaces.
DataPack
Help models understand environmental context, object state changes, temporal continuity, and the semantic cues that make scenes coherent.